Stock Market Expectations Research Articles

How should I know? Lack of confidence biases stock market expectations toward zero

Consumers’ expectations of stock market movements are important for understanding individual decisions about consequential financial outcomes and forming economic policy. Across three studies, we posit a new relationship underlying reported stock market expectations: that respondents lack confidence about their ability to forecast stock market movements and this lack of confidence biases reported probabilities toward 0 %. In Study 1, using 10 years of nationally representative survey data, we show that stock market expectations are more pessimistic than warranted when compared to actual stock market movements. In Study 2, we measure stock market expectations in several nationally representative survey experiments over 12 monthly waves (n = 4,613 participants providing 21,670 survey responses) where participants are randomly assigned to report the chances that the stock market will be “lower” or “higher” over the next month and year. Reported probabilities are biased toward 0 % in each question frame, yielding a “framing effect” gap of >10 percentage points each wave. In Study 3, we find that confidence moderates this framing effect: when we manipulate confidence regarding one's ability to forecast the stock market, there is a smaller gap between “lower” and “higher” frames for those participants who have greater confidence. To our knowledge, this is the first research showing that a lack of confidence biases reported stock market probabilities toward 0 %. It also uncovers a framing effect that is counter to psychological theory on “valence framing” and helps explain prior research showing that people are pessimistic about the stock market.

The Impact of the Fed’s Monetary Policy in 2022 on China’s Stock Market: Evidence from SSEC and SZSE

International financial markets have been abuzz with worry over the Fed’s interest rate rise strategy. The Fed’s interest rate raises policy’s implementation will have wide-ranging effects, particularly for an emerging market nation like China. The influence of the Fed’s interest rate hike policy on the Chinese market will be examined in this essay in several different ways, including stock market yields, volatility, economic growth, and market expectations. The results of this study imply that the Chinese stock market has been negatively affected over the long term by the Fed’s interest rate hike. In particular, the returns of the Shanghai Securities Composite Index and Shenzhen Securities Composite Index were significantly negatively impacted by the Fed’s rate hike, with the SZSE index being more adversely impacted. Additionally, this study discovered that the Fed’s interest rate hike had no effect on the SZSE Index’s market volatility throughout the study period but had a considerable impact on the market volatility of the SSEC Index. This implies that a Fed rate hike may result in greater SSEC Index market volatility while having a negligible effect on SSEC Index market volatility. In order to stabilize market mood and reduce risks, the government and authorities should closely monitor any potential effects of the Fed’s interest rate hike on the Chinese stock market. To adjust to the evolving global financial climate, investors should carefully evaluate market risks and develop prudent investment strategies.

How do Stock Market Expectations Condition Incumbents’ Response to Radical Technological Change?

How do Stock Market Expectations Condition Incumbents’ Response to Radical Technological Change?

Just “blah blah blah”? Stock market expectations and reactions to COP26

Applying an event study methodology, this research examines whether and how the stock market incorporated the key outcomes and statements from the COP26 summit into share prices. Our study is based on a sample of 7587 firms from four economic areas (EU, USA, China and India) belonging to the most carbon-intensive sectors. The empirical evidence shows that stock market reaction depends on how country authorities respond to commitments to accelerate and scale the transition to a greener economy, confirming that the stock market reacts negatively to stringent climate policies and positively to less stringent regulations. At the same time, in sectors emitting the most pollution, investors tend to reward companies with the best/worst environmental performance according to the type of climate policies adopted, more or less strict. Since finance is expected to play a critical role in the transition to a low-carbon economy, our results have relevant policy implications by highlighting that only clearly-defined, long-term and credible climate-related policies can lead equity investors to adequately consider environmental issues.

An Empirical Study on Determinants of Price Earnings Ratio: Evidence from Listed Food, Beverage and Tobacco Companies in Colombo Stock Exchange

The Price earnings ratio compares a stock’s price to earnings. By showing the relationship between a company’s stock price and earnings per share (EPS), the Price earnings ratio helps investors to value a stock and gauge market expectations. The ratio is affected by several factors that are responsible for the variations of Price earnings ratio. These variations of Price earnings ratio have significant impact on investor’s perception. This paper attempts to identify the factors and the relationships between the factors and Price earnings ratio of food, beverage, and tobacco companies in Colombo Stock Exchange (CSE). Based on simple sampling, data were taken for this purpose from annual reports of 30 food, beverage, and tobacco companies listed on the CSE for the five-year period from 2015 to 2019. The study focuses on secondary data collected through the published annual reports of the sample. Descriptive statistics, correlation analysis and multiple regression analysis are used to accomplish the objective of this paper. Results revealed that dividend payout ratio and leverage ratio are significant determinants of Price earnings ratio where these both variables have positive influence on Price earnings ratio. Furthermore, return on equity and earnings per share are negative insignificant determinants of Price earnings ratio. Dividend payout ratio and leverage ratio have positive correlation with price earnings ratio. It can be statistically concluded that dividend payout ratio and leverage ratio have positive significant relationship with price earnings ratio. Return on equity and earnings per share have negative correlation with price earnings ratio. Significant values are higher than the test alpha values. Therefore, the researcher can reject the alternative hypothesis. It can be statistically concluded that return on equity and earnings per share have negative insignificant relationship with price earnings ratio. This paper is evidence for fundamental analysts and decision makers to evaluate determinants that explain variations in P/E ratio of food, beverage, and tobacco companies in Sri Lanka.

Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30

With the increased financial fragility, methods have been needed to predict financial data effectively. In this study, two leading data mining technologies, classification analysis and association rule mining, are implemented for modeling potentially successful and risky stocks on the BIST 30 index and BIST 100 Index based on the key variables of index name, index value, and stock price. Classification and Regression Tree (CART) is used for classification, and Apriori is applied for association analysis. The study data set covered monthly closing values during 2013-2019. The Apriori algorithm also obtained almost all of the classification rules generated with the CART algorithm. Validated by two promising data mining techniques, proposed rules guide decision-makers in their investment decisions. By providing early warning signals of risky stocks, these rules can be used to minimize risk levels and protect decision-makers from making risky decisions.

What matters in the annuitization decision?

We perform a simultaneous test for several rational and behavioral factors known to affect the uptake of life annuities in a sample of Americans. We also investigate whether analysts’ short-term stock market expectations affect the decision to annuitize retirement wealth. We find that facing such expectations without trusting them lowers the purchase of annuities. Moreover, we find that individuals who trusted financial analysts’ expectations were less likely to purchase annuities. We attribute these findings to the availability heuristic and present bias, respectively. Finally, we discuss the mediating role of annuity antipathy. Our results provide guidance for policy-makers and annuity providers and offer venues for future research.

Missing the Target: A Review of Mark Roe's New Book on U.S. Stock‐Market Short‐Termism

The narrative of pervasive short‐termism caused by the stock market, and the need to protect corporations and the economy from it, is now a well established one. Frenetic short‐term trading and aggressive activists chasing immediate increases in profit prevent corporations from thinking about the requirements for their own long‐run success. Institutional investors squeeze cash out of the corporate sector by pressuring companies into excessive share buybacks instead of supporting long‐term investments in R&D and skills development. The relentless pressure for short‐term results also causes companies to neglect long‐term social problems like climate change, human rights, and inequality as they seek to meet the stock market's expectations for quarterly earnings.That this set of views is so widely accepted, and allowed to pass unchallenged, even in relatively sophisticated business and policy‐making circles, makes Mark Roe's new book particularly important and timely. In a measured, balanced, and utterly non‐polemical fashion, and using the highest quality academic evidence, the Harvard Law professor weighs the evidence for and against the charge that stock‐market short‐termism is a serious problem for the U.S. economy. Spoiler alert: he finds that there is no compelling case to answer. Stock‐market‐driven short‐termism is at most a minor problem.This finding matters because rather than railing against share buybacks, putting sand in the wheels of shareholder activism, and seeking to insulate corporate managers from investor pressure, Professor Roe contends that our policymakers should instead be focusing action on areas that are much more likely to have positive outcomes for society. Chief among them have been failure to resist the persistent decline in government‐funded (not corporate sector) R&D or to beef up regulation and regulatory staffing to ensure that companies bear the costs of externalities that are currently offloaded onto society.While ignoring these opportunities, we continue to devote too much attention to policies and actions—such as efforts to tax buybacks and limit shareholder activism—that are bound to be ineffective at best, and counterproductive at worst. Missing the Target is essential reading for policymakers, regulators, and anyone else interested in corporate governance, financial markets, and regulation that can and will actually make a positive difference to the rest of us.

Understanding probabilistic expectations – a behavioral approach

I propose a simple heuristic to explain how people estimate the probability of an event that has binary outcomes, such as a boom vs. a recession. Central to this approach is a psychological trait called “base-rate neglect” — a tendency for people to ignore prior probabilities in favor of more salient diagnostic information. When making predictions, people’s decisions are experience-based, but are influenced by base-rate neglect. I construct an algorithm to replicate this behavior, and find that its predictions match key features of the Anxious Index in the Survey of Professional Forecasters and stock market expectations in the Surveys of Consumers. If investors use this heuristic to form expectations in a consumption-based asset pricing model, both the equity premium and its volatility will be higher than those in a rational expectations model.

Stock Market Prediction using LSTM

Stock market comprises of different purchasers and merchants of stock. Stock market expectation implies deciding the future extent of market. A framework is fundamental to be constructed which will work with most extreme exactness and it ought to consider terrifically significant variables that could impact the outcome. Different investigates have effectively been done to anticipate securities exchange costs. The examination is done over business and software engineering space. At some point the securities exchange in all actuality does well in any event, when the economy is falling on the grounds that there are different purposes behind the benefit or loss of an offer. Foreseeing the exhibition of a securities exchange is extreme as it considers different elements. The principle point is to distinguish the feelings of financial backers. It is typically troublesome as there should be thorough investigation of public and worldwide occasions. An investor actually must know the current cost and estimation of the future price.

Dynamics and heterogeneity of subjective stock market expectations

Between 2004 and 2016, we elicited individuals’ subjective expectations of stock market returns in a Dutch internet panel at bi-annual intervals. In this paper, we develop a panel data model with a finite mixture of expectation types who differ in how they use past stock market returns to form current stock market expectations. The model allows for rounding in the probabilistic responses and for observed and unobserved heterogeneity at several levels. We estimate the type distribution in the population and find evidence for considerable heterogeneity in expectation types and meaningful variation over time, in particular during the financial crisis of 2008/09.

Transparent structured products for retail investors

Structured investment products (SPs) are derivative securities whose return is contingent on the return of their underlying assets, such as a certain stock market index. SPs have been criticized for being complex and costly on the inside, while attracting retail investors with emotionally appealing promises, on the surface, to provide tempting yields and protection for the capital invested. To circumvent such criticism, we consider transparent SPs (TSPs), which simply offer a lower and upper limit on annual return (after costs and fees) as well a transparent rule defining the return based on the return of the underlying asset. We study TSPs using both empirical and theoretical approaches. An empirical survey of real investors with best-worst scaling as well as theoretical analyses based on utility theory and multi-stage stochastic programming (MSSP) show that moderately priced TSPs are competitive in comparison with other investment products, such as index funds. Furthermore, retail investors actually exhibit substantial preference for TSPs with partial capital guarantees, over and above SPs with the superficially tempting, full capital guarantees. A theoretical, MSSP-based analysis similarly confirms that including TSPs in an investment portfolio can yield substantial gains in certainty equivalent annual return. The results further indicate that perceived gains from TSPs are sensitive to costs, market imperfections, and interest rates, as well as private preferences and stock market expectations of retail investors. This demonstrates how MSSP can be applied to financial engineering for successful implementation of TSPs in future financial markets.

Does the U.S. president affect the stock market?

Previous research shows that Democrat- and Republican-leaning investors hold different stock market expectations. In this paper, I identify a novel channel through which political opinions affect investor behavior. Instead of political affiliation, I consider nonpartisan evaluations of the executive from presidential approval rating polls. I find that large net disapproval over the U.S. president’s job is followed by low stock returns, especially in times of high political uncertainty and low market-wide sentiment. Notably, this mechanism explains away Santa-Clara and Valkanov’s (2003) “presidential puzzle.” Overall, the findings suggest that nonpartisan political views have a substantial impact on stock prices.

Financial Literacy is Associated with Stock Market Expectations But Not with Forecast Accuracy: Evidence from Germany

Financial Literacy is Associated with Stock Market Expectations But Not with Forecast Accuracy: Evidence from Germany

Market expectation shifts in option-implied volatilities in the US and UK stock markets during the Brexit vote

This paper investigates the effect of the Brexit vote on the connection between UK stock market expectations and US stock market returns. To gauge UK stock market expectations, the option-implied volatilities of the FTSE 100 index are calculated in the period starting five months before and ending four months after the Brexit referendum. To keep the analysis “clean”, it stops right before the 2016 US presidential elections. It uses an OLS regression to estimate the change in the relationship between US and UK stock market expectations.The main findings show that the US and UK stock markets became somewhat less integrated four months after the Brexit referendum compared to the five months before it. The S&P 500 Index returns have a statistically significant impact on implied volatilities of the FTSE 100 only before the Brexit referendum. However, the British risk-free rate (LIBOR) became a statistically significant factor affecting FTSE 100 implied volatilities only after Brexit. This analysis may be used by decision-makers in the money management industry to act appropriately during Black Swan events. When UK citizens unexpectedly voted in favor of Brexit, the risk-free rate dropped, making it cheaper to invest, increasing the Sharpe ratios of equity portfolios. Coupled with increased uncertainty, this caused portfolio reallocations. In turn, expected volatility measured by options-implied volatility increased. AcknowledgmentThe authors would like to thank Olesia Verchenko for critique, a KSE M.A., external defense reviewer for helpful comments.

COVID-19 and the forward-looking stock-bond return relationship

ABSTRACT The COVID-19 pandemic has caused stock market crashes and collapse of economic activities in many countries. As a result, many investors changed their stock and bond market expectations. This study investigates whether the number of COVID-19 confirmed cases influences the forward-looking stock-bond correlations. We apply a quantile approach that is beneficial to explore non-linear relationships between the forward-looking stock-bond return correlations and the COVID-19 cases. The correlations are estimated using the DCC-GARCH model for 21 financial markets from three regions (North American, Asia-Pacific, and Europe). We present empirical evidence that there are heterogeneous responses across regions and countries. Specifically, the negative stock-bond correlations weaken as the number of COVID-19 cases in the regions of North America (the U.S. and Canada) and Asia-Pacific (Australia and Japan) increases. Our results suggest that the number of COVID-19 cases is not important. Investors sell risky stocks and buy safe Treasury bonds at the beginning of the pandemic, while they adjust their portfolios risk levels when they obtain more information. Our result also highlights that this pattern is not observed in European countries.

A Dynamic Wave-Vix Approach to Investing

The VIX was introduced to the financial markets in 1993 by Dr. Robert Whaley and is a measure of the stock market's expectation of volatility based on S&P 500 index options. The VIX has become popular as a hedge against volatility. In this paper we test using the Elliott Wave Oscillator and relative VIX reading to move stocks into cash. The results are promising.

Ownership, Learning, and Beliefs

AbstractWe examine how owning a good affects learning and beliefs about its quality. We show that people have more extreme reactions to information about a good they own compared with the same information about a nonowned good: ownership causes more optimistic beliefs after receiving a positive signal and more pessimistic beliefs after receiving a negative signal. Comparing learning to normative benchmarks reveals that people overextrapolate from signals about goods they own, which leads to an overreaction to information; in contrast, learning is close to Bayesian for nonowned goods. We provide direct evidence that this effect is driven by ownership channeling greater attention toward associated information, which leads people to overweight recent signals when forming beliefs. The relationship between ownership and beliefs has testable implications for trade and market expectations. In line with these predictions, we show that the endowment effect doubles in response to positive information and disappears with negative information, and demonstrate a significant relationship between ownership and overextrapolation in survey data about stock market expectations.

Prognosticate of the financial market utilizing ensemble-based conglomerate model with technical indicators

The financial exchange is known for its outrageous multifaceted nature and instability, and individuals are continually searching for a precise and successful approach to control stock trading. The exact expectation of stock cost could assume a significant job in helping speculators improve stock returns. The motivation behind this paper is to precisely foresee the pattern of stock costs, giving a reference model to the pattern of financial exchange and the following technique for stock value expectation, and offer some incentive reference for a look into the estimating model of securities exchange and speculators investment choice. Research using a hybrid model to anticipate the stock market will have a fundamental improvement that stood out from using a single model to forecast the same. The proposed model is a regression-based ensemble model where specialized technical indicators are used as input features to anticipate the 1-day future opening cost of individual stocks. In this paper, we had taken five regression-based machine learning algorithms (Lasso, SVR, RidgeCv, K-NN, random forest) to make ensemble-based hybrid models that are a combination of them. The combined ensemble models are Lasso + SVR + RidgeCv, Lasso + SVR + RandomForest, K-NN + SVR + RidgeCv, K-NN + SVR + RandomForest, K-NN + Lasso + RandomForest, Lasso + Random Forest + RidgeCv, Lasso + K-NN + RidgeCv, Lasso + K-NN + SVR. We have selected three stock indexes for our experiment, i.e., S&P 500, DJIA, HSI. During our research, we found that the combined predictive model "Lasso + SVR + RidgeCv" is a generalized model that can perform best on all three datasets. The proposed model gives the best MSE, MAE, RMSE values are 0.0308, 0.1348, 0.1756. Our proposed model can bring some direction and reference for some investors and market controllers. Our study offers down to earth experiences and possibly helpful headings for additional examination concerning how machine learning can be viably utilized for stock market investigation and expectation.

A behavioral understanding of stock market return expectations: from evaluation frequency and financial planning horizon perspective

The continued shift to defined contribution plans and individual investment responsibility has made understanding what motivates individual behavior critical. Using 2010-2016 panel data from the Health and Retirement Study, this paper evaluates how stock market evaluation frequency and investment horizon influences stock market return expectations, which in turn influences stock ownership. Those who follow the market somewhat closely believe the probability that the market will fall or rise by more than 20% next year is higher than those who do not follow the market. Those whose financial planning horizon is one year or less have higher expectations the stock market will lose 20% or more next year. Overall expectations of high volatility (average probability the market rises or falls by 20% or more next year) are increased by somewhat closely following the stock market and reduced by longer financial planning timeframes. Higher expectations of the market losing 20% or more results in lower rates of stock ownership. The results of this study contradict the neoclassical assumption that more informed investors would have more accurate stock market expectations. Instead, investors who evaluate the market more frequently increasingly overestimate the probability of both extreme gains and losses. As market expectations influence stock market participation and decision making, financial professionals can help clients reduce myopic and hyperbolic estimation and encourage stock ownership by reducing evaluation frequency and focusing on longer-term planning.