Historical Stock Returns Research Articles

Comparative Stock Performance Analysis of Leading Electric Vehicle Brands: Tesla, BYD, and NIO Using Python Programming Language

This research paper aims to perform a comparative stock performance analysis of three leading electric vehicle brands: Tesla, BYD, and NIO, utilizing the Python programming language. The primary objective is to examine the financial trajectories of these companies by analyzing their historical stock prices, volatility, and return on investment over a defined period. Python programming language is also a key part of data analysis and data visualization. Methodologically, the study employs various Python libraries for data collection, preprocessing, and analysis, ensuring a robust and efficient analytical process. The key findings reveal distinct performance patterns and market behaviors for each company. Tesla demonstrated high volatility but significant long-term returns, while BYD showed consistent growth with moderate volatility. NIO, as a newer entrant, exhibited rapid growth with higher short-term risks. The conclusions drawn from this study provide valuable insights into the financial health and market positioning of these EV giants. By leveraging Python's powerful data analysis capabilities, this research not only enhances understanding of stock performance in the EV sector but also offers a practical framework for investors and analysts.

Stochastic Calculus and Its Impact On Analyzing Option Pricing

This research study the behaviour of the correction of Black-Scholes portfolios based on historical stock price data. We build a model that simulates one sample path of the stock price stochastic process at discrete time steps and track the correction over a time interval as it relates to the change in stock price over time. We also study the effect that relaxing this assumption has on the self-financing property of the replicating portfolio. We show that as the frequency of the time steps increases, the correction is more likely to be close to 0. We also show that the majority of historical stock return series that studied have caused the replicated portfolio to have a positive correction. We conclude that the Black-Scholes model can be used to find the no-arbitrage rational price for an option, a financial instrument that derives its value from the value of an underlying asset.

Relative informative power and stock return predictability: a new perspective from Egypt

Purpose This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms. Design/methodology/approach The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model. Findings The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners. Practical implications This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies. Originality/value In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

Improved inference in financial factor models

Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama–French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In this paper, we show that using weighted least squares (WLS) or adaptive least squares (ALS) to estimate model parameters generally leads to smaller HC standard errors compared to ordinary least squares (OLS), which translates into improved inference in the form of shorter confidence intervals and more powerful hypothesis tests. In an extensive empirical analysis based on historical stock returns and commonly used factors, we find that conditional heteroskedasticity is pronounced and that WLS and ALS can dramatically shorten confidence intervals compared to OLS, especially during times of financial turmoil.

Predicting long‐run risk factors of stock returns: Evidence from Australia

AbstractThis study utilises the stock market data provided by the Australian Equity Database to analyse the long‐run relationship between Australian stock returns and key macroeconomic variables over the period 1926–2017. To measure the diverse risk factors in the stock market, we examine the possible determinants in four main categories: real, financial, domestic and international. Our results reveal that historical stock returns are strongly connected to financial and international factors as compared to real and domestic factors. Both the 1973–1974 OPEC Oil Price Crisis and 2007–2008 Global Financial Crisis had dampening effects on stock returns. There is a positive association between the US and Australian stock markets in the long‐run. These findings on stock market dynamics and their linkages with domestic and international macroeconomic policy changes in the long‐run have important implications for traders and practitioners.

Risk and Return : A Case of Selected Automobile Stocks Listed at the National Stock Exchange of India

Risk factors, arising due to the dynamic business environment, are expected to have a great influence on the stock market returns. Investors need to collect timely information on these risk factors so that investments can be revisited, and necessary revision should be done to reduce their impact on investment returns. This research attempted to study risk and return of segments of different automobile companies listed on the National Stock Exchange of India. Returns were measured in terms of historical stock returns and reward to risk ratio. Risk was measured as standard deviation and beta. The automobile segments selected for the study were cars, LCVs/HCVs, motorcycles, scooters, and tractors. The share price data of 15 companies were collected for a period from 2009 – 2019. The returns and risk were measured for four-time tenures, namely 1-year tenure, 3-year tenure, 5-year tenure, and 10-year tenure. The basic objective for this was to ascertain the risk and return variation across time and segments. The literature evidence showed that only a very few studies attempted to study the change in risk and returns employing different time tenures. Maruti Suzuki India Ltd. recorded the highest 5-year returns (20%) with the least risk (388%) in the passenger segment. Ashok Leyland Ltd. recorded the highest 5-year returns (18%) with the least total risk (574%) and a positive beta value of 1.11 in the LCVs/HCVs segment. Eicher Motors Ltd. recorded the highest 10-year rate of return (41%) with a total risk of 521% and market risk of 0.77 in the motorcycle segment. Atul Auto Ltd. recorded the highest 10-year returns (30%) with a total risk of 714% and market risk of 0.54 in the scooters and three-wheeler segment. Escorts Ltd. recorded a 5-year return of 41% with a least total risk of 655% and had a 5-year market risk of 1.07 in the tractors segment.

Baltic Dry Index와 Interest Rates를 반영한 포트폴리오 선정 모형의 설계와 투자성과 분석

This study suggests improved investment strategy based on Markowitz’s portfolio selection model reflecting the two economic indicators: Baltic Dry Index (BDI) and interest rate. We propose BDI portfolio selection model and IR portfolio selection model reflecting the BDI and the interest rate respectively, which are based on the Markowitz’s model. First, the two portfolio selection models actively adjust the total proportion of capital invested in risky assets conservatively or aggressively on the rebalancing date depending on the BDI growth rate and the interest rate, respectively. Next, the two models actively adjust the proportion of capital invested in each risky asset, depending on the BDI sensitivity of each stock and the interest rate sensitivity of each stock. We empirically evaluate the performance of the proposed models in 12 different stock markets using historical stock return data. During the 11 years investment period from 2009 to 2019, the proposed BDI portfolio selection model and IR portfolio selection model outperformed the classic Markowitz’s portfolio model for all performance measures. This paper is differentiated from previous literature on portfolio selection models in that we propose the new portfolio selection models reflecting the BDI and the interest rate, which are the leading macro indicators but has not been covered in the previous literature.

Too high to get it right: The effect of cannabis legalization on the performance of cannabis-related stocks

This paper analyzes the historical stock returns of 10 medicinal cannabis companies for the period between 2015 and 2020, when the legalization of recreational cannabis was debated in Canada. The results of our analysis indicate that the industry was responsive to the announcements of regulatory change that led towards legalization. Furthermore, we compare the performance of cannabis companies to that of similar businesses. The results of the comparative analysis indicate that the performance of the matched pairs is largely correlated over time. Additionally, the pre-legalization performance of cannabis companies was better than their post-legalization performance. Given that cannabis companies did not outperform their matched pair, we can confirm that legalization did not have a significantly positive effect on the industry. We can also infer that, from a stock market perspective, the legalization of recreational cannabis has been unsuccessful thus far.

4분기 GDP효과를 반영한 포트폴리오 선정 모형의 투자 성과 분석

This study suggests an improved investment strategy based on Markowitz’s portfolio selection model reflecting the “End-of-the-Year (EOY) Effect”, a strong negative correlation between end-of-the-year global GDP and average stock returns. We propose three GDP portfolio selection models reflecting the EOY Effect, which are based on Markowitz’s model. First, GDP portfolio selection model 1 actively adjusts the total proportion of capital invested in risky assets conservatively or aggressively on the rebalancing date depending on the end of the year global GDP. The GDP portfolio selection model 2 actively adjusts the proportion of capital invested in each risky asset depending on the GDP sensitivity of each stock. Lastly, GDP portfolio selection model 3 is a combination of the two models. We empirically evaluate the performance of the proposed models in the US, UK, Australia, and Hong Kong stock markets using historical stock return data. During the 11-year investment period from 2009 to 2019, the proposed GDP portfolio selection models outperformed the classic Markowitz’s portfolio model in performance measures, especially in terms of Sharpe ratio. The GDP portfolio selection model 3 achieved the highest Sharpe ratio because of an interaction effect caused by the combination of the two models. This paper is differentiated from previous literature on portfolio selection models in that we propose the new portfolio selection models reflecting GDP, which is one of the key macroeconomic indicators that have not been covered in previous literature.

TWO STAGE DECUMULATION STRATEGIES FOR DC PLAN INVESTORS

Optimal stochastic control methods are used to examine decumulation strategies for a defined contribution (DC) plan retiree. An initial investment horizon of 15 years is considered, since the retiree will attain this age with high probability. The objective function reward measure is the expected sum of the withdrawals. The objective function tail risk measure is the expected linear shortfall with respect to a desired lower bound for wealth at 15 years. The lower bound wealth level is the amount which is required to fund a lifelong annuity 15 years after retirement, which generates the required minimum cash flows. This ameliorates longevity risk. The controls are the withdrawal amount each year, and the asset allocation strategy. Maximum and minimum withdrawal amounts are specified. Specifying a short initial decumulation horizon, results in the optimal strategy achieving: (i) median withdrawals at the maximum rate within 2–3 years of retirement (ii) terminal wealth larger than the desired lower bound at 15 years, with greater than [Formula: see text] probability and (iii) median terminal wealth at 15 years considerably larger than the desired lower bound. The controls are computed using a parametric model of historical stock and bond returns, and then tested in bootstrap resampled simulations using historical data. At the 15 year investment horizon, the retiree has the option of (i) continuing to self-manage the decumulation policy or (ii) purchasing an annuity.

Robust estimation of superhedging prices

We consider statistical estimation of superhedging prices using historical stock returns in a frictionless market with $d$ traded assets. We introduce a plug-in estimator based on empirical measures and show it is consistent but lacks suitable robustness. To address this, we propose novel estimators which use a larger set of martingale measures defined through a tradeoff between the radius of Wasserstein balls around the empirical measure and the allowed norm of martingale densities. We then extend our study, in part, to estimation of risk measures, to the case of markets with traded options, to a multi-period setting and to settings with model uncertainty. We also study convergence rates of estimators and convergence of super-hedging strategies.

Two Stage Decumulation Strategies for DC Plan Investors: 'A Goal Properly Set Is Halfway Reached'

Optimal stochastic control methods are used to examine decumulation strategies for a defined contribution (DC) plan retiree. An initial investment horizon of fifteen years is considered, since the retiree will attain this age with high probability. The objective function reward measure is the expected sum of the withdrawals. The objective function tail risk measure is the expected linear shortfall with respect to a desired lower bound for wealth at fifteen years. The lower bound wealth level is the amount which is required to fund a lifelong annuity fifteen years after retirement, which generates the required minimum cash flows. This ameliorates longevity risk. The controls are the withdrawal amount each year, and the asset allocation strategy. Maximum and minimum withdrawal amounts are specified. Specifying a short initial decumulation horizon, results in the optimal strategy achieving: (i) median withdrawals at the maximum rate within 2-3 years of retirement (ii) terminal wealth larger than the desired lower bound at fifteen years, with greater than $90\%$ probability and (iii) median terminal wealth at fifteen years considerably larger than the desired lower bound. The controls are computed using a parametric model of historical stock and bond returns, and then tested in bootstrap resampled simulations using historical data. At the fifteen year investment horizon, the retiree has the option of (i) continuing to self-manage the decumulation policy or (ii) purchasing an annuity.

Performance and development of a thin stock market: the Stockholm Stock Exchange 1912–2017

We estimate historical stock returns for Swedish listed companies in a newly constructed data set of daily stock prices that spans more than 100 years. Stock returns exhibit all the familiar characteristics. The growth of the public sector depressed the stock market, and the process of globalization revitalized it. Banks played an important role in the early development of the stock market. There was little trading in the past, and we examine the effects on return measurement from missing data. Stock selection and the replacement of missing transaction prices through search back procedures or limit orders make little difference to a value-weighted stock price index, while ignoring the price effects of capital operations makes a big difference.

Better portfolios with higher moments

A toolset beyond mean–variance portfolio optimization is appropriate for those instances where higher return moments might need to be taken into account, either for individual decisions or for pricing studies. Maximizing expected log surplus utility is superior for compounding returns in excess of financial obligations. Here, it is matched with a more flexible scenario representation of the investor’s joint probability distribution of returns and with an agnostic optimization engine. We show simple examples based on extrapolating historical stock and bond returns and then extended using hypothetical option prices. We clarify how Black–Scholes implied volatility anomalies can arise in a portfolio context.

Valuation Beyond CAPM: How to Calculate With Earnings Risk and Insolvency

A cash flow’s value depends on its (1) expected amount, (2) risk and (3) time of occurrence. This applies especially to uncertain payments that are generated by a company for its stake-holders. However, a cash flow’s risk is not adequately considered when it comes to valuation by the traditional DCF method. This is owed to the fact that historical stock return fluctuations, rather than the risk of cash flows, are the subject of the beta factor in Capital Asset Pricing Models (CAPM). To summarise the dark, likely-to-be-hidden secret of corporate valuation practice (see also Damodaran, 2018): a company’s true opportunities and threats (risks) are currently ignored. The failure to explicitly consider, or ‘typify’, threats and opportunities (see Henselmann, 2006, pp. 144ff., Berger and Gleisner, 2018 and Gleisner 2019e) can lead to the undervaluation of a company which, in fact, has (1) good opportunities, (2) low cashflow volatility and (3) a very good rating. This working paper clarifies the significance and effects of earnings risk, in general, and of insolvency risk, more precisely.

An empirical evaluation of dynamic vs static withdrawal strategies

Purpose The purpose of this paper is to compare simple dynamic withdrawal strategies with the static withdrawal method, examining not only failure rates and ending wealth but also spending. All withdrawal strategies are adjusted for the Internal Revenue Service’s (IRS) required minimum distribution (RMD). In addition, this study investigates the use of small company stocks (SCS) in place of large company stocks (LCS). Results indicate SCS portfolios are superior to large. When returns are poor, some dynamic strategies will not ensure income for life. This study demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies. Design/methodology/approach Using historical overlapping periods, different withdrawal strategies are examined. Previous studies focused on failure rates and ending wealth. As discussed in Milevsky (2016) different statistical distributions can have similar tail properties (prob of failure) but dissimilar risk and return profile. The detailed examination of both spending and use of small stocks advances the literature in this area. Findings Results indicate that use of small stocks is superior to using large stocks in the portfolios. When US historical stock returns are adjusted downward, there is the potential that some dynamic strategies will not ensure income for life. This study demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies. Originality/value This paper is the first to examine, in detail, annual spending results for the retiree. Second, it is shown that, overall, SCS are superior to LCS for all stock/bond allocations. Even though absolute downside risk increases slightly, this increase in downside risk is dominated by the upside potential. In other words, the positive skewness of small stock returns along with the cumulative effects of compounding at a higher rate increases both the available wealth for spending and ending wealth. Third, IRS’s RMDs are taken into account for every withdrawal strategy examined. Lastly, it demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies.

Option market trading activity and the estimation of the pricing kernel: A Bayesian approach

We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle.

Sparse and robust portfolio selection via semi-definite relaxation

In investment management, especially for automated investment services, it is critical for portfolios to have a manageable number of assets and robust performance. First, portfolios should not contain too many assets in order to reduce the management fees, transaction costs, and taxes. Second, portfolios should be robust as investment environments change rapidly. In this study, therefore, we propose two convex portfolio selection models that provide portfolios that are sparse and robust. We first perform semi-definite relaxation to develop a sparse mean-variance portfolio selection model, and further extend the model by using -norm regularization and worst-case optimization to formulate two sparse and robust portfolio selection models. Empirical analyses with historical stock returns demonstrate the effectiveness of the proposed models in forming sparse and robust portfolios.

Adaptive Market Hypothesis: Evidence from the Vietnamese Stock Market

This paper aims to test the adaptive market hypothesis in the two main Vietnamese stock exchanges, namely Ho Chi Minh City Stock Exchange (HSX) and Hanoi Stock Exchange (HNX), by measuring the relationship between current stock returns and historical stock returns. In particular, the tests employed are the automatic variance ratio test (“AVR”), the automatic portmanteau test (“AP”), the generalized spectral test (“GS”), and the time-varying autoregressive (TV-AR) approach. The empirical results validate the adaptive market hypothesis in the Vietnamese stock market. Furthermore, the results suggest that the evolution of HSX has served as an important factor of the adaptive market hypothesis.

Performance and Development of a Thin Stock Market

We estimate historical stock returns for Swedish listed companies in a newly constructed data set of daily stock prices. Stock returns exhibit all familiar characteristics. There is little trading in the past, and we examine the effects on return measurement from missing data. Stock selection and the replacement of missing transaction prices through search back procedures or limit orders make little difference to a value-weighted stock price index, while ignoring the price effects of capital operations makes a big difference. We also study stock market development. The growth of the public sector depresses the stock market, and the process of globalization revitalizes it. Banks play an important role in the early development of the stock market. The evolution of stock ownership implies that stock ownership is a normal good.