In a Minsky Moment, can financial statement data predict stock market crashes and recessions?

2021 ◽  
Author(s):  
Paige D. Bressler
Keyword(s):  
Stock Market ◽  
Financial Statement ◽  
Market Crashes

scholarly journals Can the FSCORE add value to anomaly-based portfolios? A reality check in the German stock market

2021 ◽  
Author(s):  
Eero J. Pätäri ◽  
Timo H. Leivo ◽  
Sheraz Ahmed
Keyword(s):  
Stock Market ◽  
Trading Strategies ◽  
Added Value ◽  
Financial Statement ◽  
Research Setting ◽  
Holding Period ◽  
Reality Check ◽  
German Stock ◽  
Overall Performance ◽  
German Stock Market

AbstractThis paper examines the added value of using financial statement information, particularly that of Piotroski’s (J Account Res 38:1, 2000. https://doi.org/10.2307/2672906) FSCORE, for equity portfolio selection in the German stock market in a realistic research setting in which the critique against the implementability of FSCORE-based trading strategies is taken into account. We show that the performance of annually rebalanced long-only portfolios formed on any of the examined 12 accounting-based primary criteria improves by including the FSCORE as a supplementary criterion. Our study is the first to show that although the FSCORE boost is strongest for the 1-year holding period length, it also holds, on average, for the 3-year holding period. The use of a 3-year updating frequency is particularly beneficial for the low-accrual portfolio that—when supplemented with the high-FSCORE threshold—generates the best overall performance among all 75 portfolios examined. Moreover, we show that a high FSCORE is also an efficient stand-alone criterion for long-only portfolio formation.

Rational Panics and Stock Market Crashes

1999 ◽  
Author(s):  
Pietro Veronesi ◽  
Gadi Barlevy
Keyword(s):  
Stock Market ◽  
Market Crashes

scholarly journals Influencing Factors of Stock Price in Nepal

NCC Journal ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 113-120
Author(s):  
Krishna Bahadur Thapa
Keyword(s):  
Interest Rate ◽  
Stock Market ◽  
Influencing Factors ◽  
Stock Price ◽  
Stock Exchange ◽  
Positive Association ◽  
Share Price ◽  
Financial Statement ◽  
Inverse Association ◽  
Significant Positive Association

This paper explores the influencing factors of stock price in Nepal (with reference to Nepalese commercial banks) listed on the Nepal Stock Exchange Ltd. over the period of 2008 to 2018AD. The information were collected from questionnaire and financial statement of concerned organizations and analyzed using simple linear regression model. The conclusions of the work revealed that earning per share (EPS), dividend per share (DPS), effective rules and regulations, market whims and rumors, company profiles and success depend upon luck have the significant positive association with share price while interest rate (IR) and price to earnings ratio (PER), showed the significant inverse association with share price. Further, accessibility of liquidity, fundamental and technical analysis stimulates the performance of the Nepalese stock market. More importantly, stock market has been found to respond significantly to changes in dividend and interest rate.

scholarly journals Market shocks and professionals' investment behavior – Evidence from the COVID-19 crash

2020 ◽  
Author(s):  
Christoph Huber ◽  
Juergen Huber ◽  
Michael Kirchler
Keyword(s):  
Stock Market ◽  
Risk Aversion ◽  
Risk Taking ◽  
Investment Behavior ◽  
Controlled Experiments ◽  
Price Expectations ◽  
Market Crashes ◽  
Changes In Risk ◽  
Risk Taking Behavior ◽  
Market Shocks

We investigate how the experience of stock market shocks, such as the COVID-19 crash, influences risk-taking behavior. To isolate changes in risk taking from other factors during stock market crashes, we ran controlled experiments with finance professionals in December 2019 and March 2020. We observe that their investments in the experiment were 12 percent lower in March 2020 than in December 2019, although their price expectations had not changed, and although they considered the experimental asset less risky during the crash than before. Thus, lower investments are driven by higher risk aversion, not by changes in beliefs.

Prediction of Bubbles, Crashes, and Antibubbles

2017 ◽  
Author(s):  
Didier Sornette
Keyword(s):  
Stock Market ◽  
Case Studies ◽  
Power Law ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Large Market ◽  
Simple Power ◽  
Market Crashes ◽  
Successes And Failures

This chapter examines how to predict stock market crashes and other large market events as well as the limitations of forecasting, in particular in terms of the horizon of visibility and expected precision. Several case studies are presented in detail, with a careful count of successes and failures. After providing an overview of the nature of predictions, the chapter explains how to develop and interpret statistical tests of log-periodicity. It then considers the concept of an “antibubble,” using as an example the Japanese collapse from the beginning of 1990 to the present. It also describes the first guidelines for prediction, a hierarchy of prediction schemes that includes the simple power law, and the statistical significance of the forward predictions.

Modeling Financial Bubbles and Market Crashes

2017 ◽  
Author(s):  
Didier Sornette
Keyword(s):  
Stock Market ◽  
Hazard Rate ◽  
Market Price ◽  
Imitative Behavior ◽  
Rational Model ◽  
Basic Principles ◽  
Market Prices ◽  
Speculative Bubbles ◽  
Financial Bubbles ◽  
Market Crashes

This chapter considers two versions of a rational model of speculative bubbles and stock market crashes. According to the first version, stock market prices are driven by the crash hazard that may increase sometimes due to the collective behavior of “noise traders.” The second version assumes the opposite: the crash hazard is driven by prices that may soar sometimes, again due to investors' speculative or imitative behavior. The chapter first provides an overview of what a model is before discussing the basic principles of model construction in finance. It then describes the basic ingredients of the two models of speculative bubbles and market crashes, along with the main properties of the risk-driven model. It also examines how imitation and herding drive the crash hazard rate and concludes with an analysis of the price-driven model, how imitation and herding drive the market price, and how the price return drives the crash hazard rate.

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