Search Results for « Stock market volatility »

Understanding stock market instability is a key question in financial management as practitioners seek to forecast breakdowns in asset co-movements which expose portfolios to rapid and devastating collapses in value. The structure of these co-movements can be described as a graph where companies are represented by nodes and edges capture correlations between their price movements. Learning a timely indicator of co-movement breakdowns (manifested as modifications in the graph structure) is central in understanding both financial stability and volatility forecasting. We propose to use the edge reconstruction accuracy of a graph auto-encoder (GAE) as an indicator for how spatially homogeneous connections between assets are, which, based on financial network literature, we use as a proxy to infer market volatility. Our experiments on the S&P 500 over the 2015-2022 period show that higher GAE reconstruction error values are correlated with higher volatility. We also show that out-of-sample autoregressive modeling of volatility is improved by the addition of the proposed measure. Our paper contributes to the literature of machine learning in finance particularly in the context of understanding stock market instability.

Based on our "finance-prediction-oriented" methodology which involves such elements as log-periodic self-similarity, the universal preferred scaling factor lambda=2, and allows a phenomenon of the "super-bubble" we analyze the 2009 world stock market (here represented by the SP500, Hang Seng and WIG) development. We identify elements that indicate the third decade of September 2009 as a time limit for the present bull market phase which is thus to be followed by a significant correction. In this context we also interpret the Chinese stock market index SSE. The third decade of September 2009 was accompanied with a stock market correction typically within the range of 4-5% worldwide. Taking into account the market patterns that followed the time of delivering the previous scenario we present an updated scenario whose critical time corresponds to October 28, 2009. Assuming quite evident (as of November 12, 2009) termination of the correction due to the above critical time we extend - consistently with our methodology - the stock market forecasting scenario. The corresponding expected SP500 future trend is shown in Fig. 5 and it supports a potential average continuation of increases to as far into the future as the turn of February/March 2010. We also indicate the log-periodic patterns on the gold market and they point to the end of November 2009 as the time when the trend reversal - likely local however - is expected to begin.

How the online social media, like Twitter or its variant Weibo, interacts with the stock market and whether it can be a convincing proxy to predict the stock market have been debated for years, especially for China. As the traditional theory in behavioral finance states, the individual emotions can influence decision-makings of investors, it is reasonable to further explore these controversial topics systematically from the perspective of online emotions, which are richly carried by massive tweets in social media. Through thorough studies on over 10 million stock-relevant tweets and 3 million investors from Weibo, it is revealed that inexperienced investors with high emotional volatility are more sensible to the market fluctuations than the experienced or institutional ones, and their dominant occupation also indicates that the Chinese market might be more emotional as compared to its western counterparts. Then both correlation analysis and causality test demonstrate that five attributes of the stock market in China can be competently predicted by various online emotions, like disgust, joy, sadness and fear. Specifically, the presented prediction model significantly outperforms the baseline model, including the one taking purely financial time series as input features, on predicting five attributes of the stock market under the $K$-means discretization. We also employ this prediction model in the scenario of realistic online application and its performance is further testified.

Previous studies of the stock price response to trades focused on the dynamics of single stocks, i.e. they addressed the self-response. We empirically investigate the price response of one stock to the trades of other stocks in a correlated market, i.e. the cross-responses. How large is the impact of one stock on others and vice versa? -- This impact of trades on the price change across stocks appears to be transient instead of permanent as we discuss from the viewpoint of market efficiency. Furthermore, we compare the self-responses on different scales and the self- and cross-responses on the same scale. We also find that the cross-correlation of the trade signs turns out to be a short-memory process.

This study examines how institutional differences and external crises shape volatility dynamics in emerging Asian stock markets. Using daily stock index returns for Indonesia, Malaysia, and the Philippines from 2010 to 2024, we estimate EGARCH(1,1) and TGARCH(1,1) models in a by-window design. The sample is split into the 2013 Taper Tantrum, the 2020-2021 COVID-19 period, the 2022-2023 rate-hike cycle, and tranquil phases. Prior work typically studies a single market or a static period; to our knowledge no study unifies institutional comparison with multi-crisis dynamics within one GARCH framework. We address this gap and show that all three markets display strong volatility persistence and fat-tailed returns. During crises both persistence and asymmetry increase, while tail thickness rises, implying more frequent extreme moves. After crises, parameters revert toward pre-shock levels. Cross-country evidence indicates a buffering role of institutional maturity: Malaysias stronger regulatory and information systems dampen amplification and speed recovery, whereas the Philippines thinner market structure prolongs instability. We conclude that crises amplify volatility structures, while institutional robustness governs recovery speed. The results provide policy guidance on transparency, macroprudential communication, and liquidity support to reduce volatility persistence during global shocks.