Aug 2026· Annals of Operations Research· 0 citations· 18 references
Abstract
Market timing models aim to anticipate short-term market movements according to a given source of information. Such information could be extracted from an analysis of history or a forecast of the future. In fact, the financial markets are driven mainly by the expectations of market investors and by exogenous sources. An explicit way for market investors to make clear their expectations about a certain asset is to define the implied volatility of the options that are written on that asset. Moreover, the literature proposed tools that generate the state price density of the underlying by observing the implied volatility of the options. The combination of the implied volatilities and the state price density can give deep insight into investors’ expectations about the short-term movements of the underlying and, thus, can represent a reliable source of information to perform a market timing strategy or to select a portfolio for a risk-neutral investor with a short-term horizon. To avoid adjusting the procedure for dividend-paying assets, we develop our approach considering market price indexes. This approach constitutes a completely new technique to establish both a market timing strategy and a ranking among the considered indexes. In the empirical analysis, we considered both the market timing problem for a single index and the portfolio selection problem when multiple indexes are available.
This paper investigates optimal portfolio choice for a risk-averse investor who is operating in a
financial market characterized by continuous time usage and with explicit attention being paid to
the investor's sensitivity to market movements. The investor's preferences are described by a
power utility function of constant relative risk aversion, which promotes economically interesting
behavior over wealth levels. The market consists of a risk-free asset and many risky assets,
evolving under stochastic differential equations. By allowing the investor to adjust portfolio
positions according to the changes in the processes of risky assets, the model can extend
traditional portfolio optimization frameworks. Aiming to self-assemble against dynamic
programming and the Hamilton-Jacobi-Bellman equation, exploiting Itô's calculus, we got
analytical representations of the optimal portfolio strategy and its expected terminal utility. The
quantification of the explicit sensitivity effect parameter allowed the activation of market
responsiveness to additional advantage. Numerical simulations, with Python, demonstrate the
role of market signals on a market-responsive portfolio. Two- and three-dimensional numerical
figure analyses also depict the behavior, in terms of the optimal investment decisions, of risk
aversion, asset volatility, and sensitivity parameters.
C. Achudume· International Journal of Com...· 0 citations
We propose a new model of expected stock returns that incorporates quantity information from market trading activities into the factor pricing framework. We posit that the expected return of a stock is determined by not only its factor risk exposures (beta) but also the factor's quantity fluctuations (q) induced by trading flows, and hence term the model beta times quantity (BTQ). The rationale is that sophisticated investors should demand a higher factor premium when they have absorbed noise trading flows of stocks with high loadings to that factor. The BTQ model provides a compelling risk-based explanation for stock returns, which is otherwise obscured without considering the quantity information. The cross-sectional risk-return association, which is nearly flat unconditionally, strongly depends on the quantity variable. The structured BTQ model reliably predicts monthly stock returns out of sample, and addresses the factor zoo problem by selecting a small number of factors.
Structured retail products are unsecured bonds subject to the default risk of the issuer. We analyze the price‐setting policy of issuers with respect to this default risk. Using a long‐term data set of discount certificates in the German market, we apply a time series IVX‐approach to find that (i) quoted prices do depend on issuer default risk, but (ii) this dependency is under‐proportional. Hence, retail investors are only partially compensated for bearing issuer default risk. A long‐term analysis covering the global financial crisis, the European debt crisis, and the succeeding calm period, as well as supporting evidence from the coronavirus crisis, provides patterns consistent with a fading attention hypothesis: When default risk has left the focus of retail investors, they are no longer compensated for it, even if it becomes substantial, as in the early months of the coronavirus crisis.
R. Baule, Falk Jensen· Journal of futures markets· 0 citations
Market mispricing is where the prices of a stock in the market appear not to correlate with its intrinsic or fundamental price. In traditional financial theory, and specifically the Efficient Market Hypothesis, deviations of this kind are supposed to be uncommon and short lived since rational investors are supposed to swiftly integrate the accessible data in prices. Empirical evidence has however indicated that mispricing is not present in all market’s segments equally. Capitalization and liquidity variations seem to have an effect on the level of stock price accuracy. Market capitalization ranks firms as big, medium and small-cap companies in terms of their overall market worth whereas liquidity is the way a stock can be traded without causing a tremendous movement in its price. Pricing anomalies have been identified to be more intense in small-cap and illiquid stocks than in the large-cap and well liquid stocks. The average returns have always been better than forecasted by the conventional asset pricing models when using smaller firms as they are known to give higher returns (Banz, 1981). On the same note, lower liquidity is associated with greater expected returns of stocks, thus liquidity is important in pricing assets (Amihud, 2002). In this paper, the variation of mispricing between market capitalization and liquidity segment is reviewed. It deals with the propositions of market efficient explanations, information asymmetry, and structural market restriction. The article claims that mispricing is more intense in smaller and less liquid stocks because of the presence of less or no flow of information, transaction costs and the lack of arbitrage mechanisms.
F. Kalha· Journal of economics, financ...· 0 citations
Stock market prediction is a significant research topic in the financial sector and has been widely investigated by researchers and investors. Forecasting stock markets is challenging because of the nonlinearity, volatility, and dynamics of the data. Additionally, stock markets are affected by various internal and external factors. While most previous studies relied on historical prices and technical indicators (TIs), they ignored the influence of overall economic factors on stock markets. In contrast, this study introduces a robust framework to predict daily log returns by leveraging a combined dataset comprising historical data, TIs, and macroeconomic data, including gold and oil prices, the volatility index, the dollar index, the interest rate, the 10-year Treasury yield, the term spread, and the dividend yield. Moreover, we propose a hybrid feature selection (FS) approach that combines filter and wrapper methods, unlike in previous studies, which relied primarily on a single FS approach or neglected it entirely. The dataset represents diverse sectors and firm sizes, covering five companies: Apple (AAPL Inc.), Exxon Mobil Corporation (XOM), Goldman Sachs (GS), Pfizer Inc. (PFE), and Ford Motor Company (F). We apply a hybrid evaluation approach that incorporates 5-fold time series cross-validation (TSCV) with a holdout test set to evaluate the efficiency of the proposed models. The results revealed that despite modest improvements in statistical metrics, FS significantly enhanced the economic performance of the trading strategy, as demonstrated by better returns and Sharpe ratios. The results show that deep learning (DL) models that combine macroeconomic data and the FS process, in addition to historical data and TIs, achieved higher trading returns and produced superior risk-adjusted performance, although they demonstrated slightly higher forecast errors than traditional models. This confirms that statistical accuracy alone is insufficient for evaluating financial forecasting models. This work highlights the benefits of the proposed framework for predicting stock returns across different markets, offering significant insights for financial analysts.
Aya Nabil, S. Barakat, Ahmed Aboelfetouh et al.· Scientific Reports· 0 citations
In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States based on monthly metrics of oil price uncertainty (OPU) and the broader energy uncertainty index (EUI). This approach addresses the previous literature’s limitations of narrowly focusing on crude oil prices and restricted geographic coverage by offering a more comprehensive analysis of energy uncertainty’s predictability for stock market volatility across all 50 U.S. states. We find that over the daily period of (February) 1994 to (September) 2022 and various forecast horizons, in 37 out of the 50 states, the GARCH-MIDAS model with EUI outperforms the benchmark, i.e., the GARCH-MIDAS-realized volatility (RV), which, in turn, holds for at most 18 cases under OPU. This evidence is further strengthened with the detection of higher utility gains delivered for 42 states by the GARCH-MIDAS-EUI in comparison to the GARCH-MIDAS-RV. Policymakers can utilize EUI-driven high-frequency forecasts to predict state-level economic activity, enabling timely interventions to mitigate regional recessions. For investors, incorporating broader energy market uncertainty into strategies would improve risk management, portfolio allocation, and hedging decisions, potentially enhancing risk-adjusted returns.
A. Salisu, A. E. Ogbonna, Rangan Gupta et al.· Financial Innovation· 0 citations
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