Buy-and Hold Research Articles

Does an increase in portfolio volatility create more returns? Evidence from India

The classical view of experts associates greater risks with greater rewards. The present study explores whether increased volatility in portfolios can create more returns for investors by using technical indicators or the buy-and-hold (BH) strategy. The study used closing prices of National Stock Exchange (NSE) 500 index firms for a period of 16 years (2007–2022). Five portfolios ranging from low to high volatility were created using standard deviation as a key measure. Findings indicate that as the volatility of the portfolios increases, the moving average (MA) returns seem to be higher. Across the various MA time frames, the 20-day MA seems to have generated the highest return annually (36.53% before transaction costs and 31.05% after transaction costs) due to reasonable trading opportunities with adjustable transaction costs. The CAPM also generated positive alpha (after bearing transaction costs) in the case of 20, 50, and 100 days MA, with the values being 16.66%, 13.29%, and 12.09%, respectively, in the case of highly volatile portfolios. On the other hand, while the BH strategy created substantial returns in all scenarios, the risk factor was extremely high due to the high standard deviation. Hence, it is suggested that investors/traders consider the BH strategy more cautiously while choosing between technical analysis returns and BH returns. Investors with high-risk preferences may have BH as their choice, while day traders with managed risk appetites may prefer technical tools over BH returns. AcknowledgmentThe infrastructural support provided by the FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

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.
 
 

Robust Testing for Bollinger Band, Moving Average and Relative Strength Index

We test whether the moving average indicator is profitable in trend following and compare the result with common momentum indicators such as Relative Strength Index (RSI) and Bollinger Band (BB). Our sample runs from January 1, 1963, through December 31. 2019. We test whether the signals generated are profitable and compare their success in timing the portfolios based on the previous year's volatility using the Center for Research in Security Prices (CRSP) data. We note the portfolios sorted by volatility to be trend following and use common entry points for timing them. We find all indicators to be more profitable than the Buy and Hold (BH). The moving average (MA) has the strongest return, while BB and RSI show positive return they do worse than the MA. The results hold across all portfolio deciles sorted by both size and volatility. The indicators for RSI and BB are robust to other specifications for entry and parameters. We also show price level analysis for both RSI and BB as well as a trailing three year analysis for the volatility deciles and moving average.

A hybrid framework based on extreme learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock prediction

Accurate prediction of the stock market trend can assist efficient portfolio and risk management. In recent years, with the rapid development of deep learning, it can make the classifiers more robust, which can be used for solving nonlinear problems. In our previous research, we proposed a numerical model for predicting the stock market via combination of discrete wavelet transform (DWT) denoising and extreme learning machine (ELM), and promising outcomes are achieved. The current research presents a hybrid framework using DWT, ELM, and autoencoder (AE) with feature penalty. Firstly, the backpropagation of the AE with feature penalty was deduced theoretically. Then, the raw data were denoised by DWT. The denoised data were used to train the AE with feature penalty after feature preprocessing and utilization of the labeling method. Afterward, the encoder part of the well-trained AE was utilized as the feature extraction model to train ELM model, and the hybrid framework named DAELM (DWT-AE-ELM) could be successfully developed. We also carried out experiments on the corresponding dataset of 400 stocks, and the prediction accuracy of the current study was higher than that of our previous research. According to the predicted labels, we presented an investment strategy, and the yield-to-maturity of 400 stocks was significantly higher than that of the buy-and-hold (BAH) strategy. The results confirmed the superiority of the proposed hybrid framework.

Shareholder Activism Around the Globe: Hedge Funds vs. Other Professional Investors

Shareholder activism has sharply increased over the past decade and spread both across countries and among different types of investors. Today, 50% of all engagements occur outside North America, with non-hedge fund investors accounting for one-third of all engagements. We investigate the effects and drivers of hedge fund and non-hedge fund activism using an international dataset of 2,689 activist engagements across 44 countries between 2008 and 2019. Activist investments in North America, on average, yield the largest immediate positive stock market returns and buy-and-hold returns, followed by engagements in Europe and the Asia-Pacific region. In North America, short-term abnormal returns for hedge funds are at a similar level as those for non-hedge funds, but in Europe and the Asia-Pacific region, they are higher for non-hedge funds. However, globally, hedge funds achieve higher buy-and hold returns and are more successful than non-hedge funds in implementing change in target firms. Over time, our results suggest unfulfilled investor expectations, as announcement returns are increasing but (abnormal) buy-and-hold returns and the impact on performance measures of target firms are decreasing for both hedge funds and non-hedge funds.

Adaptive calendar effects and volume of extra returns in the cryptocurrency market

PurposeThis study broadly attempts to explore adaptive or dynamics patterns of calendar effects existed in the cryptocurrency market as per the adaptive market hypothesis (AMH) framework. Another agendum of this study is to investigate the quantum of extra returns which may result from the presence of calendar effects.Design/methodology/approachThe present study considers both parametric and non-parametric approaches to verify calendar effects empirically. Specifically, this study has implemented Generalised Autoregressive Conditional Heteroscedasticity (1, 1) and Kruskal–Wallis tests in the rolling window approach to reveal adaptive patterns of calendar effects. Additionally, the present study has used the implied trading strategy to evaluate the volume of excess returns resulted from calendar effects than buy-and-hold (BH) strategy.FindingsThe overall results of the current study exhibit that calendar effect in the cryptocurrency market is dynamic rather than static which indicates the calendar effect is a time-varying phenomenon. Moreover, this study also confirmed that ITS is not suitable to obtain extra returns despite the existence of calendar effects.Research limitations/implicationsThe present study has covered some broad aspects of calendar anomalies in the cryptocurrency market, keeping aside certain other limitations which need to be addressed in the following dimensions. Future studies may aim at addressing issues like, Turn-of-the-Year effect, Halloween effect, weather effect, and Month-of-the-Year effects, and try to explore the reasons of presence of dynamic patterns of calendar effects.Practical implicationsThe significant implication of this study is that it alerts investors about market return predictability due to calendar patterns or effects in different periods. It also suggests the period in which the ITS can perform better than the BH strategy.Originality/valueIt is the first study in the cryptocurrency literature which has adopted the AMH framework to verify adaptive calendar effects or anomalies. Furthermore, this study, instead of a mere examination of the presence of calendar effects, has evaluated the potential of calendar effects to produce extra returns through trading strategies.

Using algorithmic trading to analyze short term profitability of Bitcoin.

Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable.

Application of Option Pricing Framework for Analyzing and Predicting Stock Prices in Energy Markets

In this study, the evaluation of the pricing framework for predicting West Texas Intermediate crude oil stock was implemented where detailed analysis with varying changepoint shows that an arbitrage-free forward price can be derived from the buy-and hold strategy in the energy market thereby enabling investors in the market willing to be salvage from the market uncertainties as well as Arrow-Debreu situations to execute a spot or forward contracts depending on the time and place the market becomes favorable.

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.

Performance of Six Sigma Rebalancing for Portfolios Mixing Polar Investment Styles

The paper investigates usefulness of a rebalancing strategy that was proposed in 2014 by Boďa and Roháčová and is based on ideas borrowed from the managerial concept Six Sigma. Centring upon a small investor who is willing to invest into S&P 500 Index components in an attempt to track the S&P 500 Index, the paper compares the performance of different rebalancing strategies for four different sets of monthly data ranging from 2011 to 2017. Rebalancing is undertaken on a monthly basis and tracking portfolios are diversified by investing in proportions into stocks belonging to investment styles defined by size (big/small caps) and market-to-book ratio (growth/value stocks). The results show that the Six Sigma rebalancing strategy is superior in a transaction-cost-free environment, but when transaction costs are accounted for, it is dominated by the buy-and hold strategy and a liberal threshold rebalancing strategy. Overall, periodic rebalancing fares unsatisfactorily with respect to criteria adopted for performance assessment.

Evaluating Energy Forward Dynamics Modeled as a Subordinated Hilbert-Space Linear Functional

In this study, we evaluate energy forward dynamics modeled as time-change Hilbert-space of linear functional. The energy forward is represented as an element of Hilbert-space of function. Representing energy forward and futures contracts as a time-changing stochastic process in a Hilbert-space of functions shows clearly, that an arbitrage-free forward price can be derived from the buy-and hold strategy in the energy market thereby enabling investors in the market willing to be salvage from the market uncertainties as well as Arrow-Debreu situations to execute a spot or forward contracts depending on the time and place the market becomes favorable. With a clock measuring speed of evolution or data frequency for the energy stock market, the distribution of the increments of the Lévy process with the subordinator is subordinated to the distribution of increments of the Lévy process and the results are utilized to price forward contracts of a sample electricity commodity.

Momentum Trading with the ℓ1‐Filter: Are the Markets Efficient?*

AbstractThis paper explores the possibility of generating consistent momentum profits by trading on nine major indices across the globe using the ℓ1‐filter. This methodology penalizes slope reversion of the filtered trend and identifies piecewise linear trends in the asset prices. We find the buy strategy to offer considerably higher momentum returns compared to the sell strategy. Our strategy beats the buy‐and‐hold (BH) strategy on all fronts and, thus, highlights the inefficiencies in financial markets in recent years (2000–2016). Comparing the momentum profits across a set of advanced economies (AEs) and emerging market economies (EMEs), we find that the developed and efficient financial markets of the AEs provide lower opportunities for momentum profits. The momentum profits are more than double in the EMEs as compared to the AEs. Highlighting the instability of the momentum strategy in different market states by using the global financial crisis (GFC) as a turning point, we further find that considerable opportunity exists for momentum strategies in the bullish runs that precede the crisis, as happened before the GFC. However, the momentum profits reduce significantly as the crisis sets in, increasing the degree of market uncertainty, fear, and risk‐aversiveness.

Economic benefits of technical analysis in portfolio management: Evidence from global stock markets

AbstractProducing good economic value in trading strategies for investors based on technical analysis is an issue of major interest in the academe and in practice. This study considers 9,555 trading rules and examines the usability of technical analysis. The double‐or‐out (DO) and the optimal‐portfolio (OP) strategies are used to investigate how investors construct their ass et allocation. The sample for empirical study is comprised of 20 major stock indexes from global markets as risky assets from 1998 to 2013. The DO strategy on average produces higher terminal wealth rather than does the buy‐and‐hold (BH) strategy, but the average utility (AU) of the former is worse than the latter. Nevertheless, using the OP strategy not only increases the terminal wealth of investors but also generates higher utility. Given a starting investment of one dollar and considering the best 100 trading rules, the DO and OP strategies result in average terminal wealth of 17.6 dollars and 5.9 dollars, respectively. In addition, in terms of AU, both of them are better than the BH strategy. These pieces of evidence demonstrate that investors who use an appropriate strategy of technical analysis in ass et allocation can produce good economic value, a finding that supports the continued use of technical analysis in practice.

Market Performance of Sponsor-Backed Initial Public Offerings: An Empirical Analysis of the German Market

<p><em>This paper investigates the overall market performance of Initial Public Offerings (IPOs) in Germany, by analyzing the short and long run performance of IPOs, utilizing the data from 2000-2013. Furthermore the study aims to distinguish and compare the performance of sponsor backed IPOs to non-sponsor backed IPOs, by placing a special focus on the value creating abilities of financial sponsors. The examined data set consists of 286 IPOs out of which 46 can be considered as IPOs which were backed by financial sponsor. The study suggests that, on average, IPOs significantly underperform their benchmarks. Furthermore, the evidence implies significant differences across the IPO groups with regard to performance and operational indicators. The multivariate regression shows that in the long run, private equity firms outperform their counterparts, signified by greater buy-and hold abnormal returns respectively recorded within the three-year period after the IPO. </em></p>

How Wave-Wavelet Trading Wins and Beats the Market

The market refers to the S&P 500 index SPY, which is an important benchmark of US stock performance. The following problem is of great interest: is there any trading system that can “beat” the market consistently? Proponents of the random walk hypothesis (RWH) and efficient market hypothesis (EMH) assert that stocks take an unpredictable path, and that it is impossible for a trader to outperform the overall market in the long run because stocks always trade at their fair values on stock exchanges. Therefore, the buy and hold (BH) investor has the best strategy. This paper showcases a website (GeometricWavelet.com) containing an explicit wavelet trading strategy (WT) that “beats” the market consistently, contradicting the above assertion. The data set of SPY historical prices is used to show that WT “beats” the market. This data set is then fitted with a Geometric Brownian motion (GBM). Simulation shows that WT always outperforms the fitted GBM eventually and has larger risk adjusted returns. The numbers generated show that BH’s performance is far inferior to WT’s in the long run.

Size, trading volume, and the profitability of technical trading

PurposeThe purpose of this paper is to evaluate the profitability of technical trading relative to buy-and-hold (BH) strategy at firm level, controlling for firm size and trading volume.Design/methodology/approachThis paper applies variable-length moving averages (VMAs) thoroughly to each and every stock listed on Taiwan Stock Exchange (TWSE) and computes the excess returns of technical trading relative to BH strategy. The samples are further grouped by firm size and trading volume. Furthermore, possible data snooping bias is investigated by employing Hansen’s (2005) Superior Predictive Ability tests.FindingsThe result shows that VMAs outperform the BH strategy. The profitability of VMAs, remarkably, is positively associated with size and trading volume. After correcting for data snooping bias, VMAs with longer moving averages outperform VMAs with shorter moving averages. The evidence suggests that size and volume information is accountable for trend projection.Originality/valueUnlike past studies simply applying technical trading rules to market indices, portfolios, or selected stocks, this paper evaluates the profitability of technical trading by applying VMAs comprehensively to each and every individual stock listed on TWSE controlling for the effect of firm size and trading volume, providing more practical insights for trading individual stocks.

Performance and capital structure of IPOs in Pakistan from 2000 to 2015

When a privately owned firm or privatized government entity raises capital by selling its stocks for the first time to general public, is known as initial public offering (IPO). The underpricing phenomenon and ownership structure are important characteristics of IPO process that significantly affect the short–run and long–run performance of private firm and privitized entity. This study compares the short-and long-run performance, and factors affecting these performances for both private IPOs and privatized IPOs in Pakistan. It further investigates the relationship between capital structor and initial underpricing for both group of IPOs. Event study methodology is used to compares short- and long-run performance spanning from March 2000 to June 2015 of two categories of IPOs issued in the Karachi Stock Exchange (KSE). Multivariate regression analysis is applied to examine the factors that affect the short-run and long-run performance as well as for investigating the relationship betwen underpricing and ownership structure for both group of IPOs. We find that both categories outperform in IPOs at which they are offered to investors on first day of trading, although differences in mean are insignificant. Over a five-year buy-and hold strategy, privatizing IPOs outperform and private IPOs underperform returns on the benchmark KSE 100 Stock Index. The results reveal that the size of the private firm or government entity, aftermarket risk of IPOs, and subscription ratio are significantly associated with the underpricing of IPO shares brought to market. First-day returns, market volatility and retained ownership are associated with higher five-year performance. We find that the concentration of ownership is similar, for both categories of IPOs; and significantly positive related to underpricing. The size of the firm/entity, aftermarket risk, return on assets and subscription ratio also affect ownership concentration. Same level of underpricing in both private and privatized IPOs reveals that Pakistan’s government is committed to its privatization policies as they developed capital markets by underpricing of IPOs. To make dispersion in ownership structure and to involve more small investors, the regulatory authorities such as Securities and Exchange Commission of Pakistan (SECP are required to take some steps to minimize concentration in ownership structure. There is a need of some specific range of underpricing by issuers and SECP.

Multiperiod portfolio investment using stochastic programming with conditional value at risk

This paper proposes the portfolio stochastic programming (PSP) model and the stagewise portfolio stochastic programming (SPSP) model for investing in stocks in the Taiwan stock market. The SPSP model effectively reduces the computational resources needed to solve the PSP model. Additionally, the conditional value at risk (CVaR) is used as a risk measure in the models. In each period of investment, 200 scenarios are generated to solve the SPSP model. The experimental data set consists of the 50 listed companies with the greatest market capitalization in the Taiwan stock exchange, and the experimental interval began on January 3, 2005 and ended on December 31, 2014, consisting of 2484 trading periods (days) in total. The experimental results show that the SPSP model is insensitive to small variation of the portfolio size and the historical period for estimating statistics. The portfolio size of the SPSP model can be set with two cases: M=Mc and M ≤ Mc. When M=Mc, the M invested target stocks have been predetermined. When M ≤ Mc, a set of Mc candidate stocks are given, but the M real target stocks have not been decided. The average annualized returns are 13.09% and 12.06% for the two portfolio settings, respectively, which are higher than that of the buy-and-hold (BAH) rule (9.95%). In addition, because the CVaR is considered, both portfolio settings of the SPSP model exhibit higher Sharpe and Sortino ratios than the BAH rule, indicating that the SPSP model provides a higher probability to earn a positive return. The superior predictive ability test is performed to illustrate that the SPSP model can avoid the data-snooping problem.

All in the family: The effect of family ownership on acquisition performance

We examine the acquisition performance of family and non-family firms in the S&P 500 universe. Using style-adjusted and market-adjusted buy-and-hold returns (BHAR) and controlling for firm and merger characteristics, we find that the post-merger performance of family firms is significantly better than that of non-family firms. In particular, the mean one-year style-adjusted buy-and hold abnormal return is around 17% higher for family acquirers than for non-family acquirers. Further, contrary to the argument that founding family members make value-destroying diversifying acquisitions to minimize the risk of their personal portfolio, we do not find that family firms lose value in diversifying acquisitions. This result is consistent with Stein’s model (1997) showing that diversification helps to reduce the cost of capital of the firm.

A linearized value-at-risk model with transaction costs and short selling

Though value-at-risk (VaR) has been widely applied by finance industry, a possibility of multiple optimums that is caused by the nonlinearity in modeling can challenge its application. To deal with this technical issue, we propose a linearized VaR model that employs the mixed 0-1 programing in Lin (2009) P4 model. We further advance the previous models with considering various transaction costs and the optimization of short-selling weights. We compare the performance of buy-and-hold (BH) strategy, the mean-variance (MV) model, the original P4 model, and our linearized P4 (LP4) model by rebalancing a wide scale of international and alternative investments during a period between 2001 and 2012. The results of numerical tests show the superior performance of the VaR models to the BH and the MV portfolios. The LP4 model yields the global optimum and outperforms the corresponding P4 model in both return and risk. The stability of portfolio value generated from the LP4 model supports its higher effectiveness in risk management than the P4 model.