A new behavioral finance mean variance framework

PurposeThe author proposes an update to the mean variance (MV) framework that replaces a constant risk aversion parameter using a dynamic risk aversion indicator. The contribution to the literature is made through making the static risk aversion parameter operational using an indicator of market sentiment. Results suggest that Sharpe ratios improve when the author replaces the traditional risk aversion parameter with a dynamic sentiment indicator from the behavioral finance literature when allocating between a risky portfolio and a risk-free asset. However, results are mixed when using the behavioral framework to allocate between two risky assets.Design/methodology/approachThe author includes a dynamic risk aversion parameter in the mean variance framework and back test using the traditional and updated behavioral mean variance (BMV) framework to see which framework leads to better performance.FindingsThe author finds that the behavioral framework provides superior performance when allocating between a risky and risk-free asset; however, it under performs when allocating between risky assets.Research limitations/implicationsThe research is based on back testing; therefore, it cannot be concluded that this strategy will perform well in real-time circumstances.Practical implicationsPortfolio managers may use this strategy to optimize the allocation between a risky portfolio and a risk-free asset.Social implicationsAn improved allocation between risk-free and risky assets that could lead to less leverage in the market.Originality/valueThe study is the first to use such a sentiment indicator in the traditional MV framework and show the math.

Collateral Shocks

We estimate a macroeconomic model on US data where banks lend to households and businesses and simultaneously adjust lending requirements on the two types of loans. We find that the collateral shock, a change in the ability of the financial sector to redeploy collateral, is the most important force driving the business cycle. Hit by this unique disturbance, our model quantitatively replicates the joint dynamics of output, consumption, investment, employment, and both household and business credit quantities and spreads. The estimated collateral shock generates accurate movements in lending standards and tracks measures of market sentiment. (JEL E21, E23, E24, E32, E44, G21)

Investor behavior patterns in the financial market

The article summarizes the approaches to the formation of the model of investor behavior and identifies the cause and effect relationships between the actions of individual financial market players, highlights their sustainable priorities and irrational components of behavior, as well as market conditions and experience. It is argued that in unstable conditions the behavior and opinion of individual investors is transformed into collective behavior and market sentiment on the basis of which stable advantages of investors are formed: macroeconomic status, market maturity, political and social structure, values, quality of life, human capital. The characteristics of the investor behavior model that take into account both sustainable priorities and the irrational component are presented, and a detailed classification of investor behavior models is proposed. The Asian, Islamic, Latin American and post-Soviet models are singled out and characterized, the similarities and differences between them are highlighted. It is proposed to understand the model of investor behavior as sustainable benefits (risk, expected levels of return, liquidity, propensity to invest in certain financial instruments) and the consequent consistent actions of market players, as well as a set of factors that explain it, including expectations moment of time. It is substantiated that the behavior of investors is also explained by the irrational component (anomalies, stress response, etc.). It is proved that emerging markets are more volatile than developed ones, are in a state of instability and a significant manifestation of the irrational component, which must be taken into account when assessing their behavioral characteristics. The Asian, Islamic, Latin American and post-Soviet models are singled out and characterized, the similarities and differences between them are highlighted. It is established that the Latin American and post-Soviet models of investor behavior are similar in the following characteristics: low risk tolerance, high income orientation, prominent role of the banking system, concentration of property, behavioral anomalies, manipulation and corruption, high collectivism.

COVID-19 impacts on financial markets: Takeaways from the third wave

This study provides an updated analysis of the impact of COVID-19 daily contaminations and vaccinations on the financial markets by incorporating the third wave observed in 2021. Our methodology is based on a comparative approach using a multivariate hetero­scedasticity model and data from the Eurozone and ten other countries from different economies. Our results show that COVID-19 contaminations and vaccinations strongly affected most of the countries in our sample (except for the UK, Russia and India in the case of COVID-19 contaminations). We also found that optimistic market sentiment concerning the evolution of the pandemic prevailed among the countries forming our sample (except for Switzerland, Russia and India).

Virus will complicate Central Europe’s policy dilemmas

Significance Surging inflation across CE has coincided with a rapid worsening of current-account balances, particularly in Hungary, putting the region’s currencies under strain. It has exposed the vulnerability of CE government bond markets, with yields, particularly real yields, remaining at excessively low levels. Impacts The era of low bond yields and low currency volatility in CE may have run its course. A full-fledged CE currency crisis is unlikely: Poland, Hungary and the Czech Republic are still among the most resilient emerging markets. Hungary’s crucial elections in early 2022 will reduce the scope for fiscal tightening, making policy dilemmas more acute. EU concerns about the rule of law in Poland and Hungary threaten funds earmarked for both but have had little impact on market sentiment.

Idiosyncratic volatility, investor sentiment, and returns of the GCC stock markets

Standard finance theory suggests that idiosyncratic volatility should not influence stock returns. In reality, if investors are unable to achieve efficient diversification, such risk may affect stock returns. The purpose of the study is to examine the presence of idiosyncratic volatility and sentiment in the stock markets of the GCC (Gulf Cooperation Council) countries. Monthly idiosyncratic volatility is estimated using the Fama-French three-factor model. A unified sentiment proxy for each market is created by employing Principal Component Analysis (PCA). Then, Ordinary Least Squares (OLS) regressions are applied. F-statistics, t-statistics, and adjusted R2s are used to test the presence of idiosyncratic volatility and sentiment in the GCC markets.Findings show that the effect of sentiment on stock returns is observed across all the GCC markets. Investor sentiment can weakly explain the effect of idiosyncratic volatility on stock returns. In general, investors do not price expected idiosyncratic volatility, and only the unexpected part of it affects stock returns. Overall, the first implication for investors is that they must consider market sentiment to predict the cross-section of stock prices and should not completely ignore the influence of idiosyncratic volatility on stocks. Secondly, the implication for policymakers is that they should motivate companies to go public so that investors have more options to diversify their portfolios across different sectors.

IMPACT OF COVID-19 ON CRYPTOCURRENCIES: EVIDENCE ON INFORMATION TRANSMISSION THROUGH ECONOMIC AND FINANCIAL MARKET SENTIMENTS

This paper investigates the relationship between the COVID-19 crisis and the two leading cryptocurrencies, Bitcoin and Ethereum, from 31 December 2019 to 18 August 2020. We also use an economic news sentiment index and financial market sentiment index to explore the possible mechanisms through which COVID-19 impacts cryptocurrency. We employ a VAR Granger Causality framework and Wavelet Coherence Analysis and find the cryptocurrency market was impacted in the early phase of the sample period through economic news and financial market sentiments, but this effect diminished after June 2020.

A Systematic Study on Sentiment Analysis based on Text Mining and Deep learning for Predictions in Stock Market Trends Through Social and News Media Data

Stock Market has always been one of the most active fields of research, many companies and organizations have focused their research in trying to find better ways to predict market trends. The stock market has been the instrument to measure the performance of a company and many have tried to develop methods that reduce risk for the investors. Since, the implementation of concepts like Deep Learning and Natural Language Processing has been made possible due to modern computing there has been a revolution in forecasting market trends. Also, the democratization of knowledge related to companies made possible due to the internet has provided the stake holders a means to learn about assets they choose to invest in through news media and social media also stock trading has become easier due to apps like robin hood etc. Every company now a days has some kind of social media presence or is usually reported by news media. This presence can lead to the growth of the companies by creating positive sentiment and also many losses by creating negative sentiments due to some public events. Our goal in this paper is to study the influence of news media and social media on market trends using sentiment analysis. Keywords: Deep Learning, Natural Language Processing, Stock Market, Sentiment analysis

Relationship between Herd Behavior and Chinese Stock Market Fluctuations during a Bullish Period Based on Complex Networks

Herding has a great impact on stock market fluctuations, and it is possible for researchers to analyze the herding effect due to the recent popularity of mobile Internet and the development of big data analysis technology. In this paper, we propose both investor-based and stock-based sentiment propagation networks of Chinese stock markets based on the simple pairwise correlation of posts’ sentiment indexes. And the relationship between the herding effect and Chinese stock market fluctuations is studied by comparing the network indicators with the Shanghai Securities Composite Index (SSCI) and the Causeway International Value Index (CIVIX). Through the experimental results, we find that the indicators are indeed ahead of the Chinese stock market. This study is the first attempt to model stock market sentiment by using a complex network, and it proves that investor behavior has a great effect on the stock market.