In this paper, we explore whether the adaptive markets hypothesis (AMH) describes the efficiency of the Finnish stock market better than the efficient markets hypothesis (EMH) does. Building on this, ...we also test how small market size and market liberalization impact the efficiency of the Finnish stock market and examine the relationship between market volatility and return in this market. We conduct this study by applying the subsample analysis and the rolling window analysis to the daily returns of the OMXH25 index and by measuring the efficiency through three linear and two nonlinear predictability tests. The results of our study strongly support the AMH. They also suggest that small market size alone does not make a market less efficient; opening a market to foreign investors improves its efficiency after a delay; and the correlation between market volatility and return varies over time in the Finnish stock market, being usually negative. These findings mostly contradict the traditional investment paradigm.
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BFBNIB, NUK, PILJ, SAZU, UL, UM, UPUK
Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities. ...Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the S&P 500 stocks during a five week period. We applied our analysis to estimate a discrete stock price twenty minutes after a news article was released. Using a support vector machine (SVM) derivative specially tailored for discrete numeric prediction and models containing different stock-specific variables, we show that the model containing both article terms and stock price at the time of article release had the best performance in closeness to the actual future stock price (MSE 0.04261), the same direction of price movement as the future price (57.1% directional accuracy) and the highest return using a simulated trading engine (2.06% return). We further investigated the different textual representations and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics.
We analyze the long-run relationship between the world price of crude oil and international stock markets over 1971:1–2008:3 using a cointegrated vector error correction model with additional ...regressors. Allowing for endogenously identified breaks in the cointegrating and error correction matrices, we find evidence for breaks after 1980:5, 1988:1, and 1999:9. There is a clear long-run relationship between these series for six OECD countries for 1971:1–1980.5 and 1988:2–1999.9, suggesting that stock market indices respond negatively to increases in the oil price in the long run. During 1980.6–1988.1, we find relationships that are not statistically significantly different from either zero or from the relationships of the previous period. The expected negative long-run relationship appears to disintegrate after 1999.9. This finding supports a conjecture of change in the relationship between real oil price and real stock prices in the last decade compared to earlier years, which may suggest the presence of several stock market bubbles and/or oil price bubbles since the turn of the century.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
We investigate the effect of stock market liberalization on technological innovation. Using a sample of 20 economies that experience stock market liberalization, we find that these economies exhibit ...a higher level of innovation output after liberalization and that this effect is disproportionately stronger in more innovative industries. The relaxation of financial constraints, enhanced risk sharing between domestic and foreign investors, and improved corporate governance are three plausible channels that allow stock market liberalization to promote innovation. Finally, we show that technological innovation is a mechanism through which stock market liberalization affects productivity growth and therefore economic growth. Our paper provides new insights into the real effects of stock market liberalization on productivity growth and the economy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Effect of COVID – 19 on global stock market volatility has been examined.•Factors that may help to reduce stock market volatility have also been examined.•COVID – 19 has caused significant ...volatility in global stock markets.•Economic resilience along with other economic factors reduce market volatility.•Monetary policy is less effective amid uncertain times like global pandemic.
Stock markets across the world have exhibited varying degrees of volatility following the recent COVID-19 pandemic. We have examined the effect of this pandemic on stock market volatility and whether economic strength, measured by a set of selected country-level economic characteristics and factors such as economic resilience, intensity of capitalism, level of corporate governance, financial development, monetary policy rate and quality of health system, can potentially mitigate the possible detrimental effect of the global pandemic on stock market volatility. Using data from 34 developed and emerging markets, we have found that these country-level economic characteristics and factors do help to reduce the volatility arising due to the virus pandemic. The results of this paper are important as policymakers can use these economic factors to set policy responses to tackle extraordinary heat in the global stock market in order to avoid any possible future financial crisis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We analyze time series of investor expectations of future stock market returns from six data sources between 1963 and 2011. The six measures of expectations are highly positively correlated with each ...other, as well as with past stock returns and with the level of the stock market. However, investor expectations are strongly negatively correlated with model-based expected returns. The evidence is not consistent with rational expectations representative investor models of returns.
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BFBNIB, INZLJ, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK, ZRSKP
The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the ...future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Front page economics Suttles, Gerald D; Jacobs, Mark D
2010., 2011, 20100101
eBook
In an age when pundits constantly decry overt political bias in the media, we have naturally become skeptical of the news. But the bluntness of such critiques masks the highly sophisticated ways in ...which the media frame important stories. In Front Page Economics, Gerald Suttles delves deep into the archives to examine coverage of two major economic crashes—in 1929 and 1987—in order to systematically break down the way newspapers normalize crises.Poring over the articles generated by the crashes—as well as the people in them, the writers who wrote them, and the cartoons that ran alongside them—Suttles uncovers dramatic changes between the ways the first and second crashes were reported. In the intervening half-century, an entire new economic language had arisen and the practice of business journalism had been completely altered. Both of these transformations, Suttles demonstrates, allowed journalists to describe the 1987 crash in a vocabulary that was normal and familiar to readers, rendering it routine.A subtle and probing look at how ideologies are packaged and transmitted to the casual newspaper reader, Front Page Economics brims with important insights that shed light on our own economically tumultuous times.
We investigate whether individual experiences of macroeconomic shocks affect financial risk taking, as often suggested for the generation that experienced the Great Depression. Using data from the ...Survey of Consumer Finances from 1960 to 2007, we find that individuals who have experienced low stock market returns throughout their lives so far report lower willingness to take financial risk, are less likely to participate in the stock market, invest a lower fraction of their liquid assets in stocks if they participate, and are more pessimistic about future stock returns. Those who have experienced low bond returns are less likely to own bonds. Results are estimated controlling for age, year effects, and household characteristics. More recent return experiences have stronger effects, particularly on younger people.
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BFBNIB, INZLJ, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK, ZRSKP
•A scientometric review on stock market forecasting was conducted over two decades.•Notable metrics of science mapping were analysed using VOSviewer software.•Critical areas in stock market ...forecasting were identified and discussed.•Modified Data Envelopment Analysis (DEA) methodology was proposed for future works.
Stock Market Forecasting (SMF) has become a spotlighted area and is receiving increasing attention due to the potential that investment returns can generate profound wealth. In the past, researchers have made significant efforts to forecast the stock market trends and predict the best time to buy, sell, or hold. The essence of past investigators’ various techniques and methods was to maximise the abundant opportunities that abound in the stock market trading and amass huge wealth from it. Over the years, no scientometric review has been conducted to scientifically map out the trends, progress, and limitations in the subject area. In this regard, this paper presents a pioneering scientometric review in SMF. It investigates a total of 220 reputable articles (2001–2021) to identify trends and patterns in stock market forecasting studies. VOSviewer software was used to conduct science mapping analysis. Actionable insights from the analysis explain significant metrics such as the top research outlets, most-cited articles, most co-occurred keywords, most influential countries, and much more. More so, a key finding in this paper is the introduction of a less computational approach that has the possibility of making a better forecast. Yet, past researchers have not thoroughly explored this option. This paper is beneficial to Early Stage Researchers (ESR), governments, funding bodies, managers, analysts, financial enthusiasts, practitioners, and investors, so as to understand the current progress and focus areas in stock market prediction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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