Asset Pricing Research Articles

Certainty of uncertainty for asset pricing

Uncertainty is known to be crucial in asset pricing, yet evidence from a comprehensive analysis of various uncertainty measures remains sparse. By machine learning, we construct a novel economic uncertainty index derived from a heterogeneous range of uncertainty measures and investigate its predictability of stock returns. Our composite uncertainty index exhibits robust in- and out-of-sample predictability of stock market returns over the one- to 12-month horizon. The predictive power stems from the volatility-orthogonal components of individual uncertainty measures and becomes more pronounced during high uncertainty and high sentiment periods. The predictability of our economic uncertainty index aligns with theoretical frameworks linking uncertainty to future investment, cash flows, and market expectations.

News and Asset Pricing: A High-Frequency Anatomy of the SDF

Abstract Utilizing real-time newswire data, together with a robustly estimated intraday stochastic discount factor (SDF), we identify and quantify the economic news that is priced. News related to monetary policy and finance on average accounts for most of the variation in the SDF, followed by news about international affairs and macroeconomic data. We also document nontrivial temporal variation in the relative importance of the news, along with marked differences in the estimated news risk premiums in the “factor zoo.” To further highlight the economic mechanisms at work, we associate the different news effects with interest rate, growth, and risk premium shocks. (JEL C58, G12, G14)

The Impact of Trading Volume on Stock Price Volatility

In the capital markets, research on the impact of market trading volume on asset prices and returns has been ongoing since the 1960s, and remains a focus of attention for scholars and investors both domestically and internationally. Studies on the relationship between volume and price primarily concentrate on the volatility of stock prices in relation to trading volume, and the correlation between stock return rates and trading volume. In empirical research on the volume-price relationship, the main theories are inseparable from the study of market information. Trading volume encompasses the market's reaction speed to information and the extent of information's impact on the market, which are significant reasons why trading volume can affect stock market prices and returns.

Research on cryptocurrency portfolio based on Markowitz model

In recent years, the booming growth of the cryptocurrency market has garnered significant attention from a diverse range of investors with the overall market capitalization experiencing continuous expansion. This surge in popularity, however, is accompanied by challenges arising from the market's inherent high volatility and complexity. The increasing diversity of available assets presents investors with a complex landscape to navigate. The quest to build an optimal investment portfolio has become a focal point, particularly as market participants seek strategies that not only capitalize on the market's potential for substantial returns but also effectively manage the inherent risks. In this paper, nine cryptocurrencies have been chosen to construct the portfolios, which include BTC, Ether, Tether, Binance, ripple, USDC, ADA, DOGE and TRX. The data selected comprises the daily price of the above assets from November 1, 2020, to October 9, 2023. This paper presents a comprehensive framework for constructing and optimizing cryptocurrency investment portfolios, by employing the Markowitz model and Sharpe ratio analysis. We examined these nine selected digital currencies to determine the optimal investment allocation. Our findings suggest that the highest sharpe ratio is achieved when each investment cryptocurrencies (according to the order above) allocates 0, 0.003, 0.762, 0.038, 0.005, 0.172, 0, 0.014 and 0.007, respectively, to each cryptocurrency. And the lower risk portfolio is obtained when each cryptocurrencies accounted for 2.740e-20, 6.315e-20, 0.5, 0, 0.001, 0.499, 0, 1.419e-04 and 4.210e-20, respectively. This study contributes to the sustainable development of the cryptocurrency market by providing valuable insights for financial institutions, policymakers, and other stakeholders. However, limitations of this research come from the small number of selected digital currencies and the exclusion of risk-free assets such as government bonds. Future research should consider expanding the sample size and incorporating risk-free rates to enhance the applicability and reliability of investment strategies. In conclusion, this research offers investors a practical framework to navigate the complexities of the cryptocurrency market and optimize their investment decisions for long-term value appreciation.

Examining the Adaptation of the Fama-French Three-Factor and Five-Factor Models in the US Market

The adaptability of the Fama-French three-factor and five-factor models in distinct markets is currently a key research topic. Some researchers are investigating whether the FF3FM and FF5FM are reasonable frameworks for explaining excess returns in stock markets by testing them with data from different markets. This paper mainly explores the adaptability of FF3FM and FF5FM in the US stock market, and adds CAPM to the comparison. This article uses the daily factors’ data and the daily average value weighted returns in 6 portfolios formed on size and book-to-market in the Kenneth French database website to construct a linear regression model to study the explanatory power of FF3FM and FF5FM in the US stock market. In order to determine whether these factors are parts of an effective asset pricing model, this paper assesses the efficacy, robustness, and reliability of factors in explaining asset returns using the Durbin-Watson test, the correlation matrix, and the sub-period analysis, which also confirm whether these factors have statistical significance, economic significance, and stability. It is found that the explanatory power of FF5FM is stronger than that of FF3FM in the US stock market. While both profitability and investment factors have relatively modest explanatory power, size and value factors offer significant explanatory power for stock returns.

Does derivative usage boost firm value in an economy with controls? Evidence from India

AbstractThe role of derivatives on firm valuation has recently become a subject of intense debate for emerging markets where there is greater asset price volatility, illiquidity, and the absence of common regulations and accounting standards. Given the lack of data availability, disclosure inconsistency and distinctive regulatory requirements for Indian firms, we assess the impact of corporate hedging via derivative use on the performance of BSE-100 non-financial firms over the period 2010–2017. Our results from keywords-based hand-collected derivative usage data show that all types of derivatives, except rate derivatives, have a significant and positive impact on firm value through channels of reduced earnings volatility and increased leverage with tax benefits. Firms with greater leverage tend to benefit from the use of rate derivatives. Still, among those highly leveraged firms, the ones who experience high earnings volatility are unable to benefit from rate derivatives. Besides, hedge accounting benefits firms in dealing with earnings volatility when they have higher derivatives exposure. As capital controls on international flows can limit the use of derivatives, we show that firms with currency risk exposure in terms of long-term foreign currency debt do benefit from rate derivatives. Our results are also robust with a matched sample for derivative users and non-users. Overall, we conclude that easing regulatory controls on derivatives dealings can be beneficial for firms in growing emerging economies.

Asset pricing and hedging in financial markets with fixed and proportional transaction costs

We establish the asset pricing and hedging principle in a financial market model, which is a specific case of the von Neumann-Gale dynamical system, with both fixed and proportional transaction costs and trading constraints. The main results are hedging criteria stated in terms of consistent valuation systems, generalizing the notion of an equivalent martingale measure.

Comprehensive Asset Pricing Tests in the Korean Stock Market

AbstractWe examine the empirical performance in the Korean stock market of three new asset pricing factor models: the Stambaugh–Yuan (2017) mispricing factor model, the Daniel et al. (2020) three‐factor model, Barillas‐Shanken (2018) six‐factor model and the Hou et al. (2021) q5‐factor model. We find that all factors in these factor models have significantly positive risk premiums and are not explained by the Fama–French six‐factor model. Compared to other prevalent models, the q5 model shows the highest maximum Sharpe ratio, mainly due to its profitability and expected growth factors. Further, the q5 model exhibits superior performance in explaining the returns of 97 anomaly portfolios in the Korean market.

How nonlinear benchmark in delegation contract can affect asset price and price informativeness

How nonlinear benchmark in delegation contract can affect asset price and price informativeness

Capital Asset Pricing Model: A Renewed Application on S&P 500 Index

Capital Asset Pricing Model: A Renewed Application on S&P 500 Index

The effect of structural oil shocks on stock returns of Indian renewable energy companies across market conditions

Purpose This study aims to evaluate the structural oil shocks effect on stock returns of Indian renewable energy companies across market conditions. Design/methodology/approach This study applies the structural vector autoregression model to estimate sources of oil shocks such as oil supply shock, aggregate demand shock and oil price-specific demand shock. In the next step, the panel quantile regression model estimates the effect of these oil shocks on stock return across market conditions. Monthly data are collected from January 2009 to December 2019. All renewable energy companies listed on the National Stock Exchange of India are considered for the analysis. Findings In the whole sample analysis, this study finds that oil shocks negatively affect stock returns in most of the market conditions except oil price-specific demand shock. In sub-groups, oil shocks driven by supply and aggregate demand also negatively affect stock return in most market conditions. This study finds the positive interaction of oil price-specific demand shock. A majority of these positive interactions happen in bearish market conditions. In the whole sample, the asymmetric effects of shocks driven from oil supply and oil price-specific demand are seen in most quantiles or market conditions. At the same time, aggregate demand shock does not affect asymmetrically. In the sub-group analysis, standalone renewable energy companies stock returns are least asymmetrically affected by these oil shocks. The asymmetries of oil supply-driven shock on stock returns of the renewable energy sub-group companies are found in most quantiles. Originality/value First, this is a company-level study of the stock returns response to the structural oil shocks in the renewable energy sector. Second, to the best of the authors’ knowledge, this type of study is the first in the Indian context. Third using panel quantile regression model along with capital asset pricing model framework, the authors investigate these effects across market conditions.

The Network Factor of Equity Pricing: A Signed Graph Laplacian Approach

Abstract The connections among firms exhibit heterogeneity, complexity, and dynamism, posing a challenge for traditional unsigned network models. This article proposes a signed graph Laplacian approach to construct a dynamic network index (DNI), quantifying the aggregate changes in the market network over time. A larger DNI indicates more significant changes in firms’ interconnectedness and in the market network structure. Firms with higher sensitivity to DNI exhibit lower expected returns. Incorporating DNI into conventional asset pricing models improves return predictability. Results are robust for multiple estimators, various factor models, and different selections of test assets. Our findings suggest that the network factor generates a significant equity risk premium.

An Application of Multi-Factor Model on Anomaly in China Stock Market

An Application of Multi-Factor Model on Anomaly in China Stock Market

Unveiling influential factors in the capital asset pricing model: A novel approach utilizing Taguchi method

This study uses a Design of Experiment (DOE) approach and the Taguchi method to examine how different factors affect the expected return within the framework of the CAPM. The Capital Asset Pricing Model (CAPM) is a financial framework that forecasts an investment’s expected return by taking into account its systematic risk. This model factors elements like the risk-free rate, market risk premium, and beta to ascertain the expected return for a given asset. Typically, historical market data and financial analysis provide the inputs for CAPM. This method, frequently used in manufacturing and engineering, is modified for use in the financial domain to evaluate the importance of variables like beta, market risk premium, and the risk-free rate. The research identifies the factors that influence investment performance and shows how much each factor contributes to the expected returns. It is possible to ascertain each factor's percentage contribution to the expected return through statistical analysis. This work improves knowledge of CAPM and creates opportunities for improving investment decision-making processes by bridging the gap between financial theory and experimental design. Financial practitioners can achieve more reliable and accurate investment return forecasts by incorporating DOE techniques into their existing analytical toolkit.

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

Do socially responsible indices outperform conventional indices? Evidence from before and after the onset of Covid‐19

AbstractAccording to traditional portfolio theories constraints, restrictions, and screens applied in portfolio selection reduces the diversification opportunities which can impact financial performance. Is this true in the case of socially responsible investment (SRI)? To answer this, present study analyzes the performance of Socially Responsible (SR) indices in comparison to conventional indices in an emerging economy. The uniqueness of the study is that it analyses the performance of Shariah, ESG, and thematic indices in a single study. Further, the study measures the impact of Covid‐19 on them. Comparative performance evaluation was conducted by using absolute return analysis and risk‐adjusted measures namely, Sharpe ratio, Treynor ratio, tracking error, information ratio, capital asset pricing model (CAPM), Fama–French three‐factor, and Carhart's four‐factor models. The Structural break was identified, hence analysis was conducted for the total period (January 2017–March 2023) and two sub‐periods, that is, pre and post‐Covid‐19 period. No significant difference was found between the returns of SR indices and conventional indices as against the benchmark index on the basis of absolute return analysis. Sharpe ratio and Treynor ratio both were having negative values for all the SR and conventional indices. Tracking error for all the SR and Conventional indices were very low. The CAPM and both multi‐factor models univocally pointed toward the underperformance of all the SR (except S&P BSE 100 ESG index which had equal performance) and both conventional indices against the benchmark index. Noteworthy point is that only Shariah indices gave the highest returns during post‐Covid period. This research will help in deepening the SRI in the capital market. Companies should increase their ESG scores and make efforts to be listed on the SR indices. Policymakers should announce some kind of rebates, or recognition for star‐performing companies in the field of sustainability to encourage other companies to adopt SR practices in their business operations. The novelty of the current study is that it adds to the socially responsible literature by analyzing the performance of Shariah, ESG, and Thematic indices and conventional indices in a single study in the fastest‐growing economy of India and analyses the impact of Covid‐19 on this performance.

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.

Asset prices in a production network

The relative importance of sectoral and aggregate productivity shocks in asset pricing is examined using a nonlinear dynamic equilibrium model where heterogeneous sectors interact in a production network. The model accounts for the heterogeneity in sectoral stock returns and endogenously generates conditional heteroskedasticity and fat tails. The equity risk premium is shown to be driven by sectoral shocks – specially to investment good producers and mining – with a limited contribution from the aggregate shock. SMM estimates of the elasticities of substitution between material inputs and between investment goods support the assumption of gross complementarity employed by previous network literature.

A comparative study of the CAPM and extended models in the Indian stock market

To explain the cross section of asset returns, there are numerous asset pricing models being used which require ideal conditions in the markets around the world. An attempt has been made in the present study to estimate and compare the different versions of capital asset princing model (CAPM) using the data of sectoral indices listed in S&P BSE in the Indian stock market. The two widely used approaches i.e. Fama-MacBeth (1973) and Pettengil et al. (1995) for conditional versions have been adopted in the study. The models were compared on the basis of Akaike Information Criterion (AIC) and it was obtained that the AIC value of the Unconditional higher moment CAPM was obtained minimum among other models. Hence it can be said as the better model than other models estimated.

Extreme Events Cause Market Efficiency or Inefficiency? Evidence from Precious Metals and Green Stock Indices

Background: Green investments offer a promising market-based approach to tackling global climate change but are susceptible to risk transmission from multiple assets. Purpose: To examine the multifractality between precious metals (Gold, Silver, Platinum) and green stock indices, such as Clean Energy Fuels, Nasdaq Clean Edge Green Energy, S&P Global Clean Energy and WilderHill Clean Energy, over the period from 1 January 2018 to 23 November 2023. The study aims to answer the following question: Did the events of 2020 and 2022 cause efficiency or inefficiency in the markets analysed? Methods: The Detrended Fluctuation Analysis (DFA) econophysics model will be used to answer the research question. Results: During the period of stability, there was a clear division between the markets, with Gold and green energy indices demonstrating market efficiency, while Platinum, Silver and some green indices exhibited price persistence, suggesting opportunities for above-average gains. However, the 2020 global pandemic led to widespread inefficiencies in all the markets analysed, with precious metals and green energy indices showing price persistence, indicating patterns that could lead to arbitrage opportunities. The persistence in returns suggested a tendency for asset prices to continue in the same direction for an extended period, offering opportunities to identify ongoing market trends for investment. In the pre-conflict period, the random walk hypothesis was rejected in all markets, with precious metals exhibiting anti-persistence in returns, while green energy indices showed significant persistence. During the conflict period, precious metals and green energy indices converged towards persistence in returns, except Platinum, which showed anti-persistence. Conclusion: This study provides valuable insights for investors and financial market professionals, highlighting the importance of understanding asset price behaviour patterns in different economic contexts.