In the wake of the recent equity market rise along with the potential for increased volatility as well as several equity market crashes over the last 30 years, a growing number of investors have become wary of putting large blocks of cash to work in the market all at once.

Background

Dollar cost averaging (DCA) is a strategy by which investors gradually put money to work in the market by investing a set amount at a certain frequency (typically monthly), instead of investing it all at once (lump sum or LS). The idea behind DCA is that a given amount of money buys more shares when prices are low and fewer shares when prices are high. Burton Malkiel[1] stated this principle in his seminal book, A Random Walk Down Wall Street:

Periodic investments of equal dollar amounts in common stocks can substantially reduce (but not avoid) the risks of equity investment by insuring that the entire portfolio of stocks will not be purchased at temporarily inflated prices. The investor who makes equal dollar investments will buy fewer shares when prices are high and more shares when prices are low. The reason why an investor is able to buy more when prices are low and less when prices are high can be explained by the following equation:

Number of Shares Purchased = Dollar Amount Invested / Price per Share

We can apply this concept to buying the whole market through an index fund, or to a selected set of securities (active management). Since the same dollar amount is being invested each month, this strategy forces investors to buy more shares at lower prices and fewer at higher prices. 

DCA is thus a type of value strategy, except across time rather than across stocks. Markets always fluctuate to a greater or lesser degree, and these up-down jumps allow the DCA strategy to achieve an average per-share (of the market or active portfolio) cost that is lower than the average of the market’s levels over time. 

This sounds brilliant but we will show that it is not. It is actually less desirable than investing all of one’s assets at the beginning of the investment period. We’ll show why. 

But first, let’s settle some confusion that often arises when discussing DCA. We are talking about when to invest a parcel of new money that comes into the investor’s possession – buy the intended portfolio now or dribble it into the market over time? We are not talking about dribbling money into the market over time because you earn or save it over time, as one does if receiving a paycheck – everyone does that, and should. You can’t invest money that you don’t have yet.

The Intuition and Some Theory Behind “Why Not Dollar-Cost Averaging”

The Basics: The expected return on stocks is higher than the return on cash

The most important intuitive reason why LS investing is better than DCA is that, according to basic economic theory, the expected return on the stock market is higher than the riskless (bill or bond) rate. This is because, for investors to choose stocks instead of riskless or low-risk assets, they must be priced to yield a positive equity risk premium (ERP). This is generally well known so we don’t need to cover it here.[2]

The ERP varies over time, but we don’t know how it will vary in the future, so it’s reasonable to assume that the ERP will be the same 11 months hence, in December, as it is at the time the money is received in January. Because the ERP is positive, the price of the stocks to be bought 11 months in the future is more likely to be higher than lower. DCA is a bet that the market will instead fall, but then rise again later – a possible outcome but one with a less than 50% probability. So you should invest your new money all at once, right now. That’s the ground-level intuition. 

Another way to express this is: you must think stocks are going to go up or you wouldn’t be buying them. Why do you think stocks will go down first? If you think they’re going to go down, why buy them at all? 

A DCA advocate would say that he’s hedging his bets – nobody knows the future direction of markets, so hedging is rational. But DCA is just a low-beta strategy at the beginning of the investment period and a high-beta strategy toward the end. Does the investor have the foresight to know that returns will evolve in that order, and not the opposite? It’s a strange way to put on a hedge. If you’re not sure whether stocks will go up or down, why not hedge by having a lower-beta strategy than the market all the time? That is the conventional way that investors diversify, by holding different assets at the same time so that the ups of one cancel out the downs of the other. 

We will show that this conventional way to diversify, asset-class diversification, is rational or optimal and that the diversification offered by DCA, time diversification, is an inferior strategy.   

What is time diversification, and is it beneficial? 

Some proponents of DCA argue that time diversification, of which DCA is an example, is beneficial because it reduces risk. A portfolio that is only partly invested is less risky, on average over time, than one that is fully invested all the time. If it is desirable to diversify between stocks and, say, cash (the low-risk asset could be bonds instead), at any given point in time, what is wrong with diversifying across time, holding more cash at the beginning of the investment period and more stocks as time passes? 

What’s wrong is that, unlike the benefits of diversification across assets at a given point in time, the supposed benefits of time diversification do not exist. Mark Kritzman, a celebrated financial analyst and investment manager, wrote in the Financial Analysts Journal in 1994 that “It is an indisputable mathematical fact that if you prefer a riskless asset to a risky asset given a three-month horizon, you should also prefer a riskless asset to a risky asset given a 10-year horizon...”[3] presuming your risk aversion does not change over time. 

This means that, whatever your favored allocation is at the beginning of the investing year given market conditions and your own risk preferences, it is the same in months 2, 3, and so forth through the end of the year. Unless there is a compelling reason, the asset mix should not change over time. A strategy that is low-risk at the beginning of the year and growing in risk over the year until it is high-risk at the end makes no sense.

DCA and beta

We can easily calculate the beta of a DCA portfolio. Let’s say that your new endowment of money arrives on January 1 and that you have no other investments. You invest 1/12 of the money right then, and leave the rest in cash, so that the portfolio’s beta is initially 1/12 of the market’s beta or 0.083. Starting February 1, it is 0.166, then 0.250, and so forth until December 1 when it becomes 1.000. The average beta of this portfolio over time is 0.54.

We can then compare the performance of this variable-beta portfolio to that of a constant, 0.54-beta portfolio. We’ll do something like this in the empirical section of this paper, but two distinguished academics have already done it for us. In 1979, the University of Chicago Booth School of Business professor George M. Constantinides showed mathematically that the variable-beta portfolio is suboptimal, relative to a constant-beta portfolio with the same average risk.[4]  Attempts to overturn this finding have relied on behavioral arguments (DCA makes you feel less exposed to risk or more confident). We find these arguments uncompelling. 

In 1994, Michael S. Rozeff, a professor at the University at Buffalo, showed, using empirical methods instead of math, that 

if the market has an expected positive risk premium, then lump-sum investing is mean-variance superior to dollar-averaging... [if one] hold[s] constant the risks of the two policies. ...[O]ver a 12-month horizon, lump-sum investing provides a higher return by 1 to 4 percent.... Lump-sum investing provides a higher return using the S&P 500 in 40 of 65 years.

Importantly, he concluded that 

Risk-averse investors who prefer dollar-averaging can accomplish the aim of risk reduction more effectively by lowering the fraction of funds invested in the risky asset and investing them all at once.

In other words, Rozeff agreed with our comment, earlier, that a portfolio with a constant beta of 0.54 performs better than one with a wandering beta that averages 0.54 over time, by a very considerable amount.  

DCA versus LS in a behavioral context 

So far, we’ve avoided behavioral models that depart from classical assumptions of strict investor rationality. We now add behavior as a factor. In 2001, Karyl Leggio and Donald Lien used prospect theory, a model from behavioral finance, to explain why DCA strategies are popular.[5]  Classical expected utility theory states that investors are risk-averse and have a strictly concave utility function; prospect theory, in contrast, proposes that the utility function is S-shaped with the concave part representing the utility function for gains and the convex part representing the utility function for losses.

Additionally, the prospect theory utility function states that investors are more dismayed by losses than they are pleased by an equivalent gain; they are asymmetrically loss-averse. Even after accounting for these utility functions from behavioral finance, Leggio and Lien showed that DCA strategies are inferior to LS investing. Additionally, and surprisingly, DCA strategies fared worse for more volatile equities, like small-cap stocks, than they did for less volatile equities, such as large-cap stocks. This was evidence against one of the basic claims made for DCA, that it is more effective during volatile time periods and for volatile asset classes.

Historical comparison of results from dollar-cost averaging (DCA) versus lump-sum (LS)

To compare DCA and LS over longer time horizons, more consistent with the time horizons we think our investors have, we do our own simulations, using a 20-year investment period as well as one-year investment periods. 

We tested the two strategies over the period from January 1, 1926 through September 30, 2025. The initial portfolio was assumed to be $1,000,000 in cash and the only investments available were cash and the S&P 500 index[6] (the S&P 90 until March 1957). Throughout this analysis, “the S&P” means the total return (including dividends) on the S&P index. The strategies are explained below:

DCA Strategy: 1/12th of the initial portfolio was invested in the S&P each month, at the beginning of the month. This meant that $83,333 was invested on January 1 of a given year and an additional $83,333 was invested on the first of each month until, by December 1, the entire $1,000,000 was invested in equities. 

Lump-Sum Strategy: The entire $1,000,000 portfolio was invested in the S&P at the beginning of the 1st month (the portfolio was completely invested by day 1) and held without modification for the rest of the year. 

For the purposes of this study, we assumed zero transaction costs. This assumption favors the DCA strategy since, by design, the DCA strategy involves much more trading, which results in higher transaction costs. The objectives of this backtest were twofold:

  1. We identify which strategy was historically superior by comparing portfolio values at the end of the 12th month for all of the rolling one-year periods (rolled monthly) in the period studied. Thus, we computed the returns for each strategy for 1186 rolling 12-month periods, the first one from 1 January 1926 to 31 December 1926, and the last one from 1 October 2024 to 30 September 2025.

  2. We calculate the average difference between the dollar amounts of the two strategies for rolling 20-year investment periods. In each 20-year period, we used DCA only in the first year (actually the first 11 months), so that after the end of the first year, both the DCA and LS strategy were fully invested in the S&P index for the subsequent 19 years. There was a total of 958 such 20-year periods with the first one running from 1 January 1926 to 31 December 1945 and the last one running from 1 October 2005 to 30 September 2025.

To indicate which strategy performed better historically, we assigned a value of ‘1’ to the DCA strategy if it had won (had a larger portfolio value) by the end of the 12th month, and ‘0’ if lost. We likewise assigned a ‘1’ to the LS strategy if it had won by the end of the 12th month, and a ‘0’ if it had lost. Note that the performance of the two portfolios was identical after one year because both were fully invested in the S&P; they only differed in the first year. It is critical to remember this last part because we’d expect the performance difference to be much less, on an annualized basis, over a 20-year period where the two portfolios were identical for 19 of the 20 years than it is over one-year holding periods where the portfolios were profoundly different for most of the year. 

This methodology was repeated for every 20-year holding period, rolled monthly (that is, 1 January 1926—31 December 1945 through 1 October 2005—30 September 2025) and we added up the ‘1’s for each strategy. The results of this part of the study are presented in Exhibit 1.

Exhibit 1:

Historical Success Rates Over Rolling 20-Year Periods — LS vs. DC

As can be seen in Exhibit 1, LS investing outperformed the DCA strategy in 696 out of the 958  periods (73% of the time). Nearly three out of four times, one would have been better off investing a lump sum than using a DCA strategy.

On average, at the end of a 20-year period, an investor who chose the LS strategy would have had $398,770 more (per $1 million initially invested) than an investor who chose the DCA strategy. The average ending dollar amounts over 12-month and 20-year rolling periods for both the LS and the DCA strategy can be seen in Exhibit 2. Since the strategies are fully invested by the end of the first year, both strategies have the same exact returns from the beginning of year 2 through the end of year 20. All of the outperformance is a result of the difference between the strategies during the first year; remember that, during this first year, the LS strategy is fully invested and the DCA strategy is gradually invested. On average, over each 12-month rolling period (that had a corresponding 20-year period), LS outperformed DCA by $59,732. The $398,770 average difference at the end of the 20 years corresponds to this average difference of $59,732 obtained at the end of the first year, compounded up at the rate of return of the S&P index. 

While these differences may not seem huge, they are between 6% (the one-year case) and 40% of initial capital invested. The remarkable 20-year outcome is entirely driven by the first year’s difference, compounded at the S&P’s return for the next 19 years. (In annualized terms, the LS strategy has only a 0.22% per year advantage over DCA because the large first-year benefit is spread over the remaining 19 years.) Thus, the initial advantage of the LS strategy has a profound effect on terminal wealth, almost certainly larger than one might guess from the small annualized “alpha.” 

We admit that the beta of the DCA strategy is a little lower and that is responsible for some of the lost return. However, note Rozeff’s observation that LS investing dominated (is mean-variance superior to) DCA even after holding risk constant for the two strategies. Thus, the added return from not practicing DCA is more than what can be explained by the difference in the betas of the two strategies.

LS Versus DCA in a Representative Single Year 

It is instructive to home in on a single representative or typical year. Exhibit 3 shows a one-year example, the 12-month period from 1 July 1978 through 30 June 1979, in which LS outperformed DCA by $61,347 (per $1 million invested). We call this a representative 12-month period because the difference in performance between LS and DCA in that year was similar to the average difference in performance over all the years. In other words, the performance shown in Exhibit 3 is more or less what can be expected from LS relative to DCA on average.

Exhibit 3:

Growth of Wealth: Representative 12-Month Period

Analysis of winning and losing periods for DCA 

Exhibits 4 and 5 show that, in the 20-year periods when DCA outperformed LS (approximately 30% of the time), the magnitude of that outperformance was less than when LS outperformed DCA. Specifically, during the 696 20-year periods in which LS did better than DCA, the average cumulative outperformance was $822,017 on our initial $1 million investment. During the 262 20-year periods in which DCA did better than LS, the average cumulative outperformance was $725,583.

We can combine the information in these exhibits to calculate the overall advantage of LS over DCA:

Overall advantage of LS  = {[Probability of LS outperforming DCA] x [Outperformance of LS conditional on LS outperforming DCA]}  - {[Probability of DCA outperforming LS]  x [Outperformance of DCA conditional on DCA outperforming LS]}.[8]

To sum up this analysis, the lower frequency of DCA outperformance, coupled with the lesser amount of outperformance when DCA outperformed, resulted in average 20-year outperformance of LS over DCA of $398,770, as already noted.

LS versus DCA in a “lost decade” of flat but volatile markets

While the findings we have presented so far make a compelling case for a lump-sum approach over the long term, how do the results compare over a shorter, more recent time period when markets were unrewarding? After all, one might guess that, during periods of flat but volatile markets, a policy of buying more when prices are low and less when prices are high (that is, DCA) would be successful. 

We ran the same analysis as earlier, but only for rolling 12-month periods during the decade from January 2001 to December 2010, when the S&P returned a mere 1.41% annualized, with significant volatility along the way.

The results are summarized in Exhibit 6 below.

As Exhibit 6 shows, even over this “lost decade” for the equity markets, LS still beat DCA approximately 64% of the time. The last line of Exhibit 6 shows that an investor would have ended up with $12,847 more (on an initial investment of $1,000,000) with LS than with a DCA approach over this period.

Exhibit 7 breaks down the 109 rolling periods into: 

(a) the 70 periods in which LS outperformed DCA, and  (b) the 39 periods in which DCA outperformed LS 

and examines the magnitude of average outperformance in each case. Exhibit 8 presents a summary of the data in Exhibit 7.

Exhibit 8:

Relative Outperformance by Strategy: Rolling 12-Month Periods

Although, over this 10-year “lost decade,” the margin of outperformance of DCA over LS during DCA’s winning periods was greater than the outperformance of LS over DCA during LS’s winning periods ($104,919 versus $78,458), it was not large enough to compensate for the fact that DCA would have won only 36% of the time. In short, and consistent with results from our earlier analysis going back to 1926, LS was still the superior choice during this “lost decade.” 

It is also interesting to note that, over the 70 periods within the lost decade in which LS outperformed DCA, there were only four in which the S&P 500 had a negative return over the same period. And, in the 39 periods in which DCA outperformed LS, there were only five in which the S&P 500 had a positive return over the same period. These observations reinforce the notion that DCA tends to perform better when markets are going down and LS when markets are going up.

Why is DCA still popular?

Nonetheless, there is a general misconception among many investment professionals that DCA is a higher-returning investment strategy. Our research, in addition to several prior studies, has shown that this is in fact not the case. 

If not, then why is DCA still a popular investment strategy? One explanation may be investors’ aversion to risk. DCA strategies do result in lower volatility (although only part of the time). This is a direct result of the assets staying in cash, which has little to no volatility, for a longer period of time. However, if the long-term asset allocation for an investor suggests a target equity level of ‘x’ percent, is it still appropriate to invest small portions of capital until the investor reaches the target equity allocation of ‘x’? 

The answer, according to Steven Thorley, is no.[9]  His research suggests that a buy-and-hold strategy (BH), which would hold the target risky asset allocation of ‘x’ percent from day 0, results in higher expected returns and lower risk compared to a DCA strategy.

Given that the majority of academic and industry research shows the inferiority of DCA strategies (in terms of both risk and return) when compared to LS investing and BH investing, is there any rationale for investors to feel more comfortable using a DCA strategy? Leggio and Lien shed some light on this question. They suggest that DCA is a conservative investment strategy that is best suited for investors pursuing a forced saving plan that keeps them from consuming investment earnings. 

Meir Statman uses prospect theory, originated by Daniel Kahneman and Amos Tversky, to explain the behavioral preference of investors for DCA.[10]  Statman believes that investors want to minimize the regret caused by losing money due to their decision to invest in a risky asset. Statman argues that, by using a DCA strategy, investors feel removed from part of the responsibility for bad investment outcomes. 

These possible explanations for the use of the DCA strategy rely on the (at least partial) irrationality of investors. And even DCA investors are not immune to behavioral errors that, if unchecked, could sabotage their strategy. The phenomenon of loss aversion often makes DCA investors want to abandon their periodic investments when markets are going down. Many times, there is a desire to wait until they break even on their first-month investment before they invest any more capital. Ironically, it is at precisely these moments that the opportunity for future returns is greatest.

Although it is easy to discount the DCA strategy because it is not fully rational, we have to recognize that suboptimal behavior exists for a reason and is rooted in the survival instinct. Moreover, it is hard for investors to change their behavior. Instead of completely discarding the DCA strategy, Quent Capital researched alternatives that may be consistent with investors’ observed behavioral preferences while improving the expected risk/return characteristics of their investment strategy.

Can we improve on the DCA strategy instead of discarding it? 

Let’s refer to the DCA strategy described up to this point as ‘basic DCA’. Quent Capital tested two variations or possible improvements on basic DCA. What varies is the dollar amount invested each month, and what causes it to vary is the previous month’s market return. The two variations are:

Value DCA: In this strategy, instead of investing an equal amount of money each month, more money is invested in months following a month with negative returns and less money is invested in months following a month with positive returns. We call this a value strategy since an investor would be investing more after markets have gone down and less when markets have gone up. 

Momentum DCA: In this strategy, which is the opposite of the value strategy, less money is invested in months that follow a month of negative returns and more money is invested in months that follow a month of positive returns. We call it a momentum strategy since more is invested after markets go up and less is invested after markets go down. 

The amount of variation in the amount invested is based on a ‘variance factor’, a predetermined percentage used to change the amount invested. In our tests, we used a variance factor of 20%. This means that instead of investing 1/12th of the portfolio, which is $83,333, either $100,000 (120% of $83,333) or $66,666 (80% of $83,333) is invested in each month depending on the type of variation strategy (either momentum or value) and the return of the previous month (either positive or negative). 

By investing amounts greater and less than 1/12th of the portfolio, the time period over which the money is invested differs from the 12-month period in the basic DCA. The theoretical shortest period over which the money is invested is 10 months; that is, $100,000 would be invested each month so that all the money would be invested by the beginning of the 10th month. This could only happen if the market return was down (in the value strategy) or up (in the momentum strategy) for 9 months in a row. 

The theoretical longest period over which the money is invested is 15 months: $66,666 would be invested each month. A summary of these strategies is in Exhibit 9.

Without looking at empirical results, one would intuit (given what we’ve already seen about LS versus DCA) that momentum DCA is a better choice if return is the only criterion. This is because LS outperformed basic DCA in terms of return. Recall that the rationale behind this finding is that markets generally go up, and momentum DCA has a larger equity allocation, on average over time, than basic DCA.

Value DCA should result in the exact opposite outcome, because value DCA has a smaller equity allocation, on average over time, than basic DCA (and markets generally go up). 

The full-period (January 1926-September 2025) empirical results comparing the value and momentum variations to basic DCA are in Exhibit 10.

As Exhibit 10 shows, momentum DCA outperformed basic DCA in 550 of the 958 periods, about 57% of the time. In contrast, value DCA outperformed basic DCA only about 42% of the time. On average over all the rolling 20-year periods, momentum DCA outperformed the basic DCA strategy by about $20,000 per $1,000,000 initially invested, whereas value DCA lagged the DCA strategy by about $30,000. Since the momentum DCA strategy is closer than basic DCA to LS investing, we expect it to outperform basic DCA. Likewise, value DCA strategy is farther than basic DCA from LS investing, and we thus expect it to underperform basic DCA. The empirical results are in line with our prior expectations. 

Does DCA work better when markets are near all-time highs? 

Intuition suggests that, even if DCA is an inferior strategy on average across long periods of time, it might work well (improve returns in an absolute sense) when the equity market is at or near an all-time high.

To test this proposition, we re-ran the simulations described earlier but only for periods starting when a total return index of the S&P 500 (or predecessor indices) was at an all-time month-end high. Although we all know that a market at or near its all-time high can move even higher – and often does – it is behaviorally difficult to buy when you can look back and see only lower prices. So we tested the DCA-near-highs strategy just described.

Exhibit 11 shows the results. Of the 958 12-month periods that had 19 years of subsequent data (so that 20-year results could be calculated) — that is, periods starting between 31 December 1925 and 31 October 2005 — 281 of them, or 29.3%, began when the market’s total return index at an all-time month-end high. This fact alone shows how powerful the uptrend of the U.S. stock market has been, over a period that had multiple crashes, a Great Depression and some lesser depressions, and other disruptions.

Exhibit 11 shows that isolating those 281 12-month periods, that started when markets were at or near an all-time high, LS  outperformed DCA 256 times (that is, 91.1% of the time), and DCA outperformed LS only 25 times. In addition, when LS outperformed, it was by a larger margin than achieved by DCA when that latter strategy outperformed. 

Now, let’s look at the 677 12-month periods when markets were not at or near an all-time high. LS beat DCA 440 times and DCA beat LS 237 times. In addition, just as in the periods that started when the market was high, when LS outperformed, it was by a larger margin than the outperformance of DCA when that latter strategy was a winner.

Thus, even in time periods that intuitively seem best for DCA, DCA still underperformed on average.

Conclusion

DCA has been a popular investment strategy with individual investors and is still recommended by many investment professionals. Although theoretical and empirical data demonstrate that DCA is inferior to LS and BH strategies, even when the market is at or near all-time highs, it is important to understand the underlying reasons that cause investors to choose DCA and investment professionals to recommend DCA. Risk-averse investors, who may be unwilling to invest into risky assets all at once, find the piecemeal approach of DCA strategies emotionally comforting.

Investment professionals such as financial advisors find DCA to be an easy way of essentially forcing investors to commit to an investment strategy. This discipline results in greater expected future wealth for the investors than would otherwise obtain. Given that it’s the concept of DCA (rather than the result) that investors and professional still find useful, it is incumbent on investment researchers to explore better variations of DCA and we have done so.

Quent Capital research has shown that, using the momentum DCA approach, which involves investing more or less than the basic DCA amount depending on whether the market went up or down, respectively, in the previous month, results in higher returns than basic DCA. Momentum DCA is grounded in the notion of piecemeal investing that investors find appealing, but involves a slight modification that improves expected return.

Though momentum DCA is by no means an optimal solution, this small deviation from basic DCA is a significant step in reconciling rational investing principles with irrational investor behavior.

[1] Malkiel, Burton G. A Random Walk Down Wall Street. New York: Norton, 1975. p. 242.

[2]  A thorough literature review regarding the ERP is in Siegel (2017). Some updated materials, which present lower-than-historical but still very substantially positive estimates of the ERP, are on the CFA Institute Research Foundation website.

[3] Kritzman, Mark P. 2015. “What Practitioners Need to Know . . . About Time Diversification (corrected March 2015).” Financial Analysts Journal, volume 71, number 1. The original (uncorrected) article was in the January/February 1994 issue (volume 50, number 1).

[4]  Constantinides, George M. 1979. “A Note on the Suboptimality of Dollar-Cost Averaging as an Investment Policy.” Journal of Financial and Quantitative Analysis, Vol. 14, pp. 443-450.

[5] Leggio, Karyl B., and Donald Lien. 2001. “Does loss aversion explain dollar-cost averaging?” Financial Services Review, Vol. 10, pp. 117-127.

[6] The S&P 500 is used here solely as a representative benchmark for U.S. large-cap equity performance. It does not reflect the performance of any specific investment strategy or client account. It is not possible to invest directly in an index.

[7] These figures reflect historical performance based on simulated investments in the S&P 500 index. Actual results may vary. Past performance is not indicative of future results.

[8] In more conventional math notation, E[OP ] = Prob(LS > DCA) x (OPLS | LS > DCA) – Prob(DCA > LS) x (OPDCA | DCA > LS) = (72.65) x ($822,017 ) - (27.35% ) x ($725,583) = $398,770, where OP means outperformance, and the other abbreviations are as previously used.

[9]  Thorley, Steven R. 1995. “The Time-Diversification Controversy.” 1995, Financial Analysts Journal, Vol. 51, no. 3, pp. 68-76.

[10]  Statman, Meir. 1995. “A Behavioral Framework for Dollar-Cost Averaging.” The Journal of Portfolio Management, Vol. 22, no. 1, pp. 70-78. Kahneman, Daniel P., and Amos Tversky. 1979. “Prospect Theory: An Analysis of Decision under Risk.” Econometrica, Vol. 47, no. 1, pp. 263-291.

Disclosures

This material is for informational purposes only and does not constitute an offer to sell or solicitation of an offer to buy any security. Past performance is not indicative of future results. Different types of investments involve varying degrees of risk. There can be no assurance that the future performance of any specific investment strategy referenced in this letter will be profitable, or equal any corresponding indicated historical performance levels. 

Gregg S. Fisher is the founder and portfolio manager at Quent Capital, a registered investment advisor that manages small-cap investment strategies. As such, Quent Capital may benefit from increased interest in small-cap investing. 

Hypothetical Performance Disclosure: The analyses presented include hypothetical results based on back-tested models, which have inherent limitations. These results do not reflect actual trading or portfolio performance and were derived using historical index data with the benefit of hindsight. Hypothetical back-tested performance does not reflect advisory fees, transaction costs, tax considerations, or other expenses that would reduce returns. There is no guarantee that any of these results will be achieved. Actual investment results may differ materially.

Index Disclosure: References to the S&P 500 Index (and the S&P 90 for periods prior to March 1957) are for informational purposes only. The S&P 500 is an unmanaged index of large-cap U.S. equities and does not incur management fees, expenses, or transaction costs. It is not possible to invest directly in an index. Any comparison to an index is illustrative and should consider material differences in investment strategy, risk, and liquidity.

This research is intended for informational purposes only and should not be construed as investment advice. The analyses and conclusions of Quent Capital contained in this letter include certain statements, assumptions, opinions, estimates, and projections that reflect assumptions by Quent Capital concerning anticipated results that are inherently subject to significant economic, competitive, and other uncertainties and contingencies and have been included solely for illustrative purposes. No representations express or implied, are made as to then accuracy or completeness of such statements, assumptions, estimates or projections or with respect to any other materials herein. Neither Quent Capital, LLC, the Fund or any of their affiliates makes any representation or warranty, express or implied, as to the accuracy or completeness of the information contained in this letter and nothing contained in this letter should be relied upon as a promise or representation as to past or future performance of Quent Capital, LLC, the Fund or any other entity. 

Certain of the statements may be statements of future expectations and other forward-looking statements that are based on Quent Capital’s current views and assumptions and involve known and unknown risks and uncertainties that could cause actual results, performance or events to differ materially from those expressed or implied in such statements. All content is subject to change without notice.  For full disclosures, please go to our Disclosures Page.