Dollar Cost Averaging Research Articles

상장지수펀드의 성과에 영향을 미치는 상장지수펀드의 횡단면 특성

This study investigates how cross-sectional characteristics of ETFs affect the long-term returns of ETFs. We use tracking multiples, underlying market classification, tax type, manager, underlying index, volatility, replication method, classification system, index producer, and underlying asset class as cross-sectional characteristics, all of which are characterized by nominal variables. The performance of ETFs is characterized by Dollar Cost Averaging (DCA) returns, Lump-sum deposit (LD) returns, and return differential (RD=DCA-LD). Tracking multiples are categorized into 1X Inverse, 2X Leveraged, 2X Inverse, and Normal, and underlying market classifications are categorized into Domestic, Domestic & International, and International. Both tracking multiples and underlying market categorization had a cross-sectional impact on DCA returns, LD returns, and RD. Taxation type had a significant cross-sectional effect on DCA and LD returns only for non-taxable. Dividend income tax (separately taxed real estate ETFs) had an impact only when the dependent variable was DCA returns. Dividend income tax (foreign equity only ETFs) had no effect on DCA and LD returns.

Investment Behavior and Strategy in Cryptocurrency in Indonesia

Investor behavior in making investment decisions is always based on clear reasons, whether for reasons of the future, profit, or hedging. This also provides a more precise portrait of investor behavior and the learning process if you want to enter the crypto asset industry. Apart from that, future suggestions for future researchers are expected to include female informants as a new perspective in researching investor behavior. The aim of carrying out this research is to find out what strategies can be implemented by cryptocurrency asset investors in order to get an optimal level of return. The method approach used in this research is qualitative library research. The data sources used as references in this research are data originating from articles, books, and newspapers from electronic media with the search keywords: how to invest in crypto, crypto investing techniques, and crypto investing strategies. The data obtained was then analyzed using NVivo 10 Plus software. From the results of this research, several strategies for investing in cryptocurrency assets were obtained, namely: determining the type and purpose of investment, studying the cryptocurrency assets you are interested in, choosing trusted cryptocurrency assets, choosing safe exchanges and wallets, paying attention to service fees and spreads, don't be afraid of missing out ( FOMO), buying when prices are low (support) and selling when prices are high (resistance), diversifying and mitigating risk, investing on various platforms, not easily panicking and oriented towards holding on for dear life (HODL), always following development information cryptocurrency, following and joining cryptocurrency communities, and regularly doing dollar-cost averaging (DCA).

Optimization of Stock Investments in Indonesia Stock Exchange: Comparing the Method of Dollar Cost Averaging (DCA) and Lump Sum (LS)

The aim of this research is to find out how to optimize stock investment using the dollar cost averaging method and the lump sum method and also to compare the performance between the dollar cost averaging method and the lump sum method based on return results. This research uses a descriptive method with a quantitative approach. This research uses 2 companies with the largest market cap listed on the Indonesian Stock Exchange for the 2021-2023 period. The results of this research found that the lump sum method return in 2022 on BBCA shares reached 22.85% and on BBRI shares 18.19% higher than the return on the dollar cost averaging method on BBCA shares which was only 13.70% and on shares BBRI is only 10.86%. The lump sum method return in 2022 is the highest return between 2021 and 2023. These results prove that the lump sum method is more optimal than the dollar cost averaging method.

The Dollar Cost Averaging, Lump Sum, and Value Averaging Strategies in Mutual Fund Investments

Investment strategy and portfolio management are crucial considerations when investing in mutual funds. This research aims to analyze the effectiveness of achieving optimal returns by simulating dollar cost averaging, lump sum, and value averaging approaches. This analysis is conducted about the performance of a mutual fund portfolio, utilizing the Sharpe and Treynor methods. The research sample employs a purposive sampling technique, selecting the top 5 mutual funds in terms of performance, managed by different investment managers in the year 2022. Simulations are being conducted over three specific periods. In addition to mutual funds, a comparative analysis was conducted involving the Jakarta Composite Index (JCI) and 10-year government bond yields (SUN). The simulation results on equity funds for the three periods indicate that the Lump Sum investment method outperforms dollar cost averaging and value averaging, particularly over 6 years. This finding is further corroborated by the Kruskal-Wallis test, which highlights a significant variance in return performance. The results of the Sharpe and Treynor investment methods exhibit significant differences, as confirmed by the Mann-Whitney test. Lastly, regarding benchmark comparisons, the performance of equity funds surpasses that of the benchmark.

Comparison of Long-Term Investment Strategies: DCA vs Lump-Sum Investing in the S&P 500 Index

This research delves into the realm of long-term investments, a domain often navigated by unsophisticated investors who prefer a hands-off, 'set and forget' approach. Central to this investigation are two dominant investment strategies: Dollar-Cost Averaging and Lump-Sum investing. The study meticulously compares the performance of these strategies over a significant 21-year period, from January 2002 to December 2022, a timeline punctuated by major market upheavals such as the Dot Com Bubble and the 2020 Coronavirus crash. Drawing data from the reputable Yahoo! Finance on the S&P 500 index, the research offers a robust backtest of both strategies. The results are illuminating: Lump-Sum investments, especially those made during the nadir of February 2009, realized returns amplifying the initial investment by up to 5.2 times, clearly overshadowing the returns from Dollar-Cost Averaging. Intriguingly, a fusion of both strategies, a hybrid approach, showcased even more impressive gains, hinting at the merits of a diversified investment strategy. Conclusively, within the parameters of this research, Lump-Sum investing emerges as the more potent strategy. However, the study also paves the way for future inquiries, especially concerning the potential of these strategies in the turbulent waters of volatile assets, exemplified by Bitcoin.

Dollar-Cost Average plan in EMU countries: could it be beneficial? What the data say.

This study examines a real case scenario using real data under the assumption of a long-term investment horizon. We examine the Dollar Cost Average, Cost Average Plan (CAP) in our case, and attempt to ascertain whether it could prove beneficial for an investor that does not have a significant amount of money to invest when the plan begins. We compare the outcome of the CAP with the alternative scenario of investing a monthly amount in a deposits account, which is in real terms the most comparable plan to the CAP. Using data from January, 2003 to November, 2021 from a wide range of ten (10) European Monetary Union countries, we compare the empirical evidence of these results and we reach the conclusions that CAP outperforms the seasonal bank deposits plan. Moreover, we compare these strategies with the Buy-and-Hold (BnH) strategy, as most studies do, and we conclude that CAP is more beneficial for highly volatile and less uptrend markets than a BnH strategy.
 
 
 
 Jel Classifications: G1; G11; N2
 Keywords: Dollar Cost Averaging; Investment Strategies; Investment Decisions; Investment Plans.
 
 

DOLLAR COST AVERAGING RETURNS ESTIMATION

Given a geometric Brownian motion wealth process, a log-Normal lower bound is constructed for the returns of a regular investing schedule. The distribution parameters of this bound are computed recursively. For dollar cost averaging (equal amounts in equal time intervals), parameters are computed in closed form. A lump sum (single amount at time 0) investing schedule is described which achieves a terminal wealth distribution that matches the wealth distribution indicated by the lower bound. Results are applied to annual returns of the S&P Composite Index from the last 150 years. Among data analysis results, the probability of negative returns is less than 2.5% when annual dollar cost averaging lasts over 40 years.

Dollar Cost Averaging and Fair Value Dispersion

Dollar Cost Averaging and Fair Value Dispersion

Does Market Timing Beat Dollar Cost Averaging?

This paper explores several methods for investing a series of monthly cash contributions in an equity index, such as the S&P 500 or the Nikkei 225. The dollar cost averaging (DCA), three variations of market timing (MT1, MT2, and MT3), and 12-month perfect foresight (PF) are examined, and they are built on the same assumptions, such as monthly cash inflows, no borrowing of cash, and no selling of equity. The PF outcomes, unachievable by human beings, serve as the optimal boundaries. Our results show that in both the U.S. and Japanese markets, the PF dominates the DCA, while the MTs tend to deliver similar results as the DCA. Thus, the DCA seems an effective investment method.

Intelligent candlestick forecast system for financial time-series analysis using metaheuristics-optimized multi-output machine learning

The effective prediction of stock market prices and trends is a critical topic in financial research for investors and stakeholders who wish to increase their return on investment. Motivated by highly unstable stock market targets and the continuous trading system that has been launched by Taiwan’s stock market, this work develops an intelligent candlestick forecast system (ICFS), which combines the advantages of three forecasting techniques — single-point, interval value, and trend forecasting. The ICFS is simple and intuitive, using a sliding-window metaheuristic-optimized multi-output least squares support vector regression hybrid model scheme to forecast simultaneously four types of price information that are represented on a candlestick chart. Three parameter-free metaheuristic algorithms, namely teaching-learning-based optimization (TLBO), symbiotic organisms search (SOS), and forensic-based investigation (FBI), are in turn used to optimize the hyperparameters of the multi-output least squares support vector regression (MLSSVR) model, to increase its accuracy and stability in predicting stock prices. Benchmark data from relevant studies are used to compare the forecast performances of previously developed and the three proposed hybrid models. Among all tested models, the FBI-MLSSVR greatly outperforms the others in forecasting Standard & Poor’s (S&P) and National Association of Securities Dealers Automated Quotations (NASDAQ) indexes, Taiwan’s exchange-traded funds, and major listed construction companies. A floating dollar-cost averaging strategy for maximizing returns while minimizing investment risks is proposed and compared with traditional dollar-cost averaging and the buy-and-hold strategy. The profitability of the predictions made using the proposed system is verified against actual stock market operations. The ICFS is packaged as a visualized expert system and independent application program for the convenience of users, who can intuitively run it and interpret its forecasts. Therefore, this study is highly practical by providing stakeholders with an easy-to-use and accurate predictive tool.

Dollar Cost Averaging vs Optimal Buy-and-Hold with Equity Momentum and Reversals

Dollar Cost Averaging vs Optimal Buy-and-Hold with Equity Momentum and Reversals

Application of multi-criteria decision analysis for investment strategies in the Indian equity market

In the Indian equity market, the Systematic Investment Plan (SIP) is the most popular strategy due to its convenience for disciplined investing regardless of market conditions. This study analyzes the excess returns of an extensive dataset of listed Indian companies from 2010 to 2019, along with a value-based version of the Multi-Criteria Decision Analysis (MCDA), to identify top performing stocks, based on their sectors and market capitalization. The findings of the study provide empirical evidence of Value Averaging (VA) as a viable alternative strategy over SIP (also known as Dollar Cost Averaging or Rupee Cost Averaging) as 352 out of 359 companies yielded higher returns under VA. The superiority of the VA strategy over the SIP was particularly marked in the consumer goods, financial services and industrial manufacturing sectors, with a clear dominance of small cap companies. The results also show that risk factors for VA strategy play an important role and should be taken into account, rather than base investment decisions on excess returns alone. The efficiency scores of individual stocks provide important insights for mutual funds, financial brokers and individual investors in India.

Equity-linked Guaranteed Minimum Death Benefits with dollar cost averaging

In this paper, we analyze a form of equity-linked Guaranteed Minimum Death Benefit (GMDB), whose payoff depends on a dollar cost averaging (DCA) style periodic investment in the risky index, with rider premiums paid at regular intervals. This rider is a very natural insurance vehicle for equity-linked variable annuities, and the DCA feature has a tendency to reduce the uncertainty associated with the final payoff, beyond the minimum benefit guaranteed to the beneficiary upon death of the insured. This makes the insurer risk easier to manage as the contract ages. From the policyholder's perspective, the protection is cheaper than a standard GMDB whose payoff depends solely on the risky index value upon death, but still offers the upside potential from an investment made at regular intervals. We derive closed-form valuation formulas under the fairly broad class of exponential Lévy models for the risky index, which includes Black-Scholes as a special case. Closed-form valuation is provided by a fundamental link between this contract and a series of Asian options, for which valuation is well established. We provide several valuation strategies, and demonstrate the soundness of the framework.

Effectiveness of Augmented Dollar-Cost Averaging

Dollar-Cost Averaging investment strategy has been widely criticized by academics for decades. This article proposes an Augmented Dollar-Cost Averaging (ADCA) strategy that conditions investment strategy to the prevailing market environment. The ADCA investment strategy is more aggressive if the economy is expanding and more conservative if the economy is contracting. Changes in market volatility, unemployment rate and capacity utilization were used to determine whether the economy was expanding or contracting. Using the Sharpe ratio, coupled with first-order and second-order stochastic dominance criteria, this study shows the risk-reducing benefits of ADCA in the U.S. stock market between 1967 and 2018.

Do lump-sum investing strategies really outperform dollar-cost averaging strategies?

Purpose The literature has demonstrated that lump-sum (LS) outperforms dollar-cost averaging (DCA) in uptrend markets while DCA outperforms LS only when the asset price is mean-reverted or downtrend. To bridge the gap in the literature, this study aims to use both Sharpe ratio (SR) and economic performance measure (EPM) to compare the performance of DCA and LS under both accumulative and disaccumulative approaches when the asset price is simulated to be uptrend. Design/methodology/approach This study uses both disaccumulative and accumulative approaches to compare DCA with LS and uses both SR and EPM to evaluate their performance when the asset price is simulated to be uptrend. Instead of using the annualized returns that are commonly used by other DCA studies, we compute the holding-period returns in the comparison in this paper. Findings The simulation shows that no matter which approach is used, DCA outperforms LS in nearly all the cases in the less uptrend markets while DCA still performs better than LS in many cases of the uptrend markets, especially when the market is more volatile and investment horizon is long, regardless which approach the authors used. The authors also find more evidence supporting DCA over LS by using EPM, which is more suitable in the analysis because the returns generated by DCA are positive skewed and flat-tailed that are ignored when SR is used. Research limitations/implications The authors conclude that DCA is a better trading strategy than LS for investment even in the uptrend market, especially on high risky assets. Practical implications Investors could consider choosing DCA instead of LS as their trading strategy, especially when they prefer long term investment and investing in high-risk assets. Social implications Fund managers could consider recommending DCA to their customers, especially when they prefer long term investment and investing in high-risk assets. Originality/value This is the own study and, as far as the authors know, this is the first study in the literature uses both SR and EPM to compare the performance of DCA and LS under both accumulative and disaccumulative approaches when the asset price is simulated to be uptrend.

Does Value Averaging Strategy Improve Investment Performance? Evidence from the Korean Fund Market

This study analyzes whether a value averaging (VA) strategy, which adjusts the amount of investment each period to achieve the target amount of investment in risk assets, as a modified form of a dollar cost averaging (DCA) strategy, improves investment performance. Using 18.5 years of fund market data in Korea from 2001 to June 2019, we compare the investment performance of VA strategy relative to two alternatives: DCA strategy, which invests a certain amount in each period, and Buy-and-Hold (BH) strategy, which refers to half-and-half asset allocation between risky and risk-free assets and has an expected return which is the same as that of DCA in the ex-ante sense. Our historical performance analysis reveals that the VA strategy has lower average return and higher standard deviation compared to the BH strategy and has lower average return and lower standard deviation compared to the DCA strategy. These findings are in stark contrast to the claims made by advocates of VA strategy that the strategy improves investment performance.

An analysis of dollar cost averaging and market timing investment strategies

In this paper we present new theoretical and practical insights into the method of dollar cost averaging (DCA) and averaging-style investment timing strategies, with a formal analysis of the problem. Firstly, we provide a rigorous mathematical formulation for studying DCA and related strategies. This provides closed form formulae for the expected value and variance of the investor’s wealth process, which mathematically proves many properties that have been documented in the literature only by empirical studies. Secondly, we prove a counterintuitive, but important, result that the frequency of DCA investment has a non-monotonic and non-trivial impact on risk, risk-return trade-off and other important performance metrics (such as the Sharpe ratio).Thirdly, we provide a method of valuing the DCA risk for models which incorporate jumps. We also provide a method of hedging DCA risk based on applying Asian options. Finally, using the PROJ method of computation, we obtain a robust and computationally efficient method for calculating standard risk measures of generic and deterministic investment strategies, such as DCA. We provide numerical experiments to illustrate our conclusions, and conduct an empirical study on the S&P500 index (from 1954 to 2019) to substantiate our results.

An Analysis of Dollar Cost Averaging and Market Timing Investment Strategies

In this paper we present new theoretical and practical insights into the method of dollar cost averaging (DCA) and averaging-style investment timing strategies, with a formal analysis of the problem. Firstly, we provide a rigorous mathematical formulation for studying DCA and related strategies. This provides closed form formulae for the expected value and variance of the investor's wealth process, which mathematically proves many properties that have been documented in the literature only by empirical studies. Secondly, we prove a counterintuitive, but important, result that the frequency of DCA investment has a non-monotonic and non-trivial impact on risk, risk-return trade-off and other important performance metrics (such as the Sharpe ratio).Thirdly, we provide a method of valuing the DCA risk for models which incorporate jumps. We also provide a method of hedging DCA risk based on applying Asian options. Finally, using the PROJ method of computation, we obtain a robust and computationally efficient method for calculating standard risk measures of generic and deterministic investment strategies, such as DCA. We provide numerical experiments to illustrate our conclusions, and conduct an empirical study on the S&P500 index (from 1954 to 2019) to substantiate our results.

A PERFORMANCE ANALYSIS OF DOLLAR-COST AVERAGING AND SELF-ANNUITIZATION

The return and risk of dollar-cost averaging (DCA) and self-annuitization (SA) investing are compared with the underlying return in this paper. The underlying return, which is assumed to be normally distributed, is generated by Monte Carlo simulations under six market scenarios including upward and mean reverting markets across several investment horizons. Owing to the multiple cash flows of DCA and SA, the annual internal rate of return is used to measure the DCA and SA returns. The results show that the mean return of DCA is slightly higher than the underlying return, while the SA is lower, particularly under short investment horizons. Both DCA and SA produce higher return volatility and riskiness than the underlying return. They also create negative skewness and excess kurtosis for the return distributions. For comparing their performances, we use the economic performance measure which can consider those high moments of distribution. Except for the mean reverting market, the underlying return is the best performer, while SA is the worst. This evidence becomes even clearer and convincing as the investment horizon increases. DCA can have lower riskiness and perform better only under the mean reverting market.

Teaching Dollar-Cost Averaging: An In-Class Student Team Simulation

This paper presents a class tested exercise requiring student teams to perform investing decisions with the objective of maximizing the portfolio value. Afterwards, dollar-cost averaging (DCA) is introduced and usually achieves a greater portfolio value than that of most teams. Students are then assigned the task of determining the lowest and highest possible portfolio values for the simulation. DCA is then assessed on its nearness to either the worst or best (optimal) value. The exercise is enjoyable for students and offers several points of learning, including that DCA is an attractive investing strategy that offers many benefits and performs well.