Probability Of Returns Research Articles

Extreme spillovers across carbon and energy markets: A multiscale higher-order moment analysis

The complexity of carbon market mechanisms and the uncertainty in market conditions raise questions on how carbon and energy markets interact. The majority of existing studies focused on the lower-order moment spillover across carbon and energy markets, thereby posing limitations on carbon risk management and carbon market efficiency. This paper analyzes multiscale skewness and kurtosis spillovers across carbon and energy markets under different market conditions. It is found that carbon market is a short-term net skewness spillover receiver, but becomes a medium-term risk source under all market conditions, capable of transmitting skewness risk to the natural gas market. The carbon market acts as a short- and medium-term kurtosis risk source for the natural gas and electricity markets in the lower probability of extreme returns, but bears the kurtosis risk from the natural gas and coal markets when the extreme risk is high. These results indicate that policymakers should take measures to adjust carbon prices when facing long-term skewness risk and a lower probability of extreme returns in the carbon market, to prevent further spread of risk to the energy markets.

ESG scores, scandal probability, and event returns

The informativeness of environmental, social, and governance (ESG) scores and their actual impact on firms remains understudied. To address this gap in the literature, we make theoretical predictions and conduct empirical research revealing that a high ESG score is associated with a lower probability of ESG scandals and lower stock returns during a scandal event. Our results suggest that ESG scores are heterogeneous but informative, and that a strong ESG reputation may have both positive and negative consequences for firms. Drawing on our findings, we develop a model and showcase that firms face an optimization problem when determining optimal ESG investment levels. Two equilibria may exist based on the trade-off between ESG scandal losses and ESG adjustment costs. Our model explains why certain firms make heterogeneous ESG decisions

Does Idiosyncratic Risk Have a Significant Impact on Return Probability? A Case Study of Borsa Istanbul 100 Stocks

Most asset pricing models ignore idiosyncratic risk, or firm-specific risk, while it is one of the most critical determinants of asset pricing and stock returns. In this paper, we investigate the impact of idiosyncratic risk on the returns of stocks traded on the Borsa Istanbul using six different fixed effect panel tobit and four different fixed effect panel logit regression models. The results of logit models suggest that as idiosyncratic risk increases, probability of positive stock returns also increases. Furthermore, an increase in a stock's market sensitivity has a negative effect on the probability of positive returns, while an increase in the market-to-book ratio, firm size, and market return has positive effects on returns. In all models, the explanatory variables, including idiosyncratic risk, market-to-book ratio, firm size, and market return, have a positive effect on returns, except for the model where negative values of dependent variable are censored at zero.

Distributional Policy Gradient With Distributional Value Function.

In this article, we propose a distributional policy-gradient method based on distributional reinforcement learning (RL) and policy gradient. Conventional RL algorithms typically estimate the expectation of return, given a state-action pair. Furthermore, distributional RL algorithms consider the return as a random variable and estimate the return distribution that can characterize the probability of different returns resulted by environmental uncertainties. Thus, the return distribution provides more valuable information than its expectation, leading to superior policies in general. Although distributional RL has been investigated widely in value-based RL methods, very few policy-gradient methods take advantage of distributional RL. To bridge this research gap, we propose a distributional policy-gradient method by introducing a distributional value function to the policy gradient (DVDPG). We estimate the distribution of policy gradient instead of the expectation estimated in conventional policy-gradient RL methods. Furthermore, we propose two policy-gradient value sampling mechanisms to do policy improvement. First, we propose a distribution-probability-sampling method that samples the policy-gradient value according to the quantile probability of return distribution. Second, a uniform sample mechanism is proposed. With our sample mechanisms, the proposed distributional policy-gradient method enhances the stochasticity of the policy gradient, improving the exploration efficiency and benefiting to avoid falling into local optimal solutions. In sparse-reward tasks, the distribution-probability-sampling method outperforms the uniform sample mechanism. In dense-reward tasks, the two sample mechanisms perform similarly. Moreover, we show that the conventional policy-gradient method is a special case of the proposed method. Experimental results on various sparse-reward and dense-reward OpenAI-gym tasks illustrate the efficiency of the proposed method, outperforming baselines in almost environments.

Dicamba-Based Herbicide Programs, Cropping Sequences, Tillage Types in Cotton – Profitability and Risk Efficiency Analysis

Herbicide-resistant weeds threaten cotton production, and herbicides alone are not a sustainable remedy. Field trials at two locations, College Station and Thrall, Texas (2019-21), assessed dicamba-based herbicide programs in various crop sequences and tillage systems to identify sustainable weed management recommendations based on weed control and partial net returns. The study showed no-till cover cropping and conventional tillage had the highest aggregate partial net returns at College Station and Thrall, respectively. A cotton:sorghum:cotton rotation provided significantly higher partial net returns (>$450 ha-1) compared to continuous cotton only at Thrall. Partial net returns were significantly higher in a low input herbicide program (LI) compared to a high input herbicide program (HI) from 2019-21 only at Thrall. Herbicide programs showed a high degree of overlap and were indistinct in all tillage types at College Station based on CDFs graphs. Low input under cotton:sorghum:cotton rotation showed 100% probability of higher partial net returns compared to continuous cotton in all tillage types at Thrall. Stoplight charts showed LI provided greater than 50% probability of partial net returns greater than $1,066 ha-1 across tillage types at College Station. In Thrall, LI under cotton:sorghum:cotton rotation offered 88% and 26% probability of partial net returns above $1,066 ha-1 in conventional and strip tillage, respectively. Higher partial net returns for LI were mostly attributed to cost savings from using inexpensive herbicides rather than higher yields. Therefore, using less expensive herbicide programs and crop rotation appeared to be the most risk efficient strategy for managing weeds in irrigated and dryland cotton production.

Rational overoptimism and limited liability

Is excessive risk-taking in credit cycles driven by incentives or biased beliefs? I propose a framework suggesting that the two are actually related and, specifically, that procyclical overoptimism can arise rationally from risk-taking incentives. I show that when firms and banks have a limited liability payoff structure, they have lower incentives to pay attention to the aggregate conditions that generate risk. This leads to systematic underestimation of the accumulation of risk during economic booms and overoptimistic beliefs. As a result, agents lend and borrow excessively, further increasing downside risk. Credit cycles driven by this new “uninformed” risk-taking are consistent with existing evidence such as high credit and low-risk premia predicting a higher probability of crises and negative returns for banks. My model suggests that regulating incentives can decrease overoptimistic beliefs and thus mitigate boom-and-bust cycles.

Analysis of Paired Trading Strategies Based on Boll Bands

The development of information technology makes quantitative investment strategy widely used by various investors. Among them, pairing trading strategy has attracted the attention of a large number of investors. In this paper, the validity of paired trading is verified and the validity of Bollinger band signals used for paired trading is tested. To be specific, this paper takes Shanghai and Shenzhen 300 component stocks and China Securities 500 component stocks as data sets. In this paper, the correlation coefficient method is used to match the stocks. This study uses the closing price data of stocks in each pairing group from August 2021 to August 2022 for training, and backtests the training results with trading data from August 2022 to February 2023. From the yield index, the backtest group can get the highest yield of 23.4%. In addition, according to the analysis, the probability of positive returns in the backtest group is up to 80%.

Short-lived product returns forecasting when customers are unwilling to return the product: A grey-graphical evaluation and review technique

Consumer electronics, such as mobile phones, laptops, and tablets, encounter the challenge of short product life cycles due to the rapid advancement of technology, marketing models, and rapidly changing consumer tastes. This study attempts to provide a forecasting model for a short-lived product for closed-loop supply chain planning. Importantly, we model the setting where the customers prefer to store the product instead of returning it for various reasons such as security and secondary use. We proposed a generic method that can be implemented for any technology-driven product's returns and remanufacturing process. We used the Graphical Evaluation and Review Technique to calculate the probability and quantity of product returns, the quantity of modules, parts, materials, and disposal. Then, we employed the Grey-Graphical Evaluation and Review Technique to calculate the expected time interval for core returns and the expected interval for the availability of module/parts/material for reconditioning/ remanufacturing of the product. An illustrative case study demonstrates the results of the forecasting model. Further, we analyzed the effect of customers' unwillingness to return the product on the reverse network. Our analysis asserts that the customer storing an end-of-use product has a greater impact on the probability of overall product return. Our analysis found that when customers tend to return the product instead of storing it for secondary use, the equivalence probability of returns can increase up to 91 %. In addition, we also address the effect of product return on the “ecological rucksack” of mobile phones. This can help policymakers design hassle-free return experiences and attractive customer incentives.

INVESTMENTS IN TIMES OF UNCERTAINTY: FORMATION OF PORTFOLIOS USING RANDOM FOREST

Objective: based on a systematic approach using machine learning , this research aims to propose a model of selection and allocation of assets that allows for building profitable and safe portfolios, even in times of insecurity and low predictability. Methodology: we used the machine learning algorithm called random forest to associate the independent variables with a dependent one and learn the probability of positive returns in the month following the data collection. According to the probabilities, the stocks were allocated into long , short, or non-allocated portfolios . Finally, we allocated a share of gold, which is a protection asset much used in times of crisis and uncertainty. Results and contributions: the study reached its goal and demonstrated being possible to build profitable and safe investment portfolios, even in times of greater uncertainty and volatility, as in 2020 due to the Covid-19 pandemic. We found that the model is effective in moments of crisis and also of greater predictability, as in the period from 2016 to 2019 when the stock exchange has an uptrend. Relevance: the relevance of this study points to an unprecedented historical context in Brazil, where uncertainties regarding both the local and world economy have demanded advanced studies of prediction to minimize risks and contribute to results for investors. In addition, we highlight that following a short period of low Selic (2019 to 2021), the Central Bank increased the rate again, raising the interest more in profitable and safer assets than the investment in stocks. Impact on the area: the study has a positive impact on the finance area since studies in this field promote greater stability to investors and thus better capital flow for companies, which, in turn, contribute to society and the growth of the country.

DOLLAR COST AVERAGING RETURNS ESTIMATION

Given a geometric Brownian motion wealth process, a log-Normal lower bound is constructed for the returns of a regular investing schedule. The distribution parameters of this bound are computed recursively. For dollar cost averaging (equal amounts in equal time intervals), parameters are computed in closed form. A lump sum (single amount at time 0) investing schedule is described which achieves a terminal wealth distribution that matches the wealth distribution indicated by the lower bound. Results are applied to annual returns of the S&P Composite Index from the last 150 years. Among data analysis results, the probability of negative returns is less than 2.5% when annual dollar cost averaging lasts over 40 years.

Ambiguity and asset prices: a closer look in an emerging market

PurposeThis study empirically documents the effect of ambiguity on stock returns in a major emerging market along with the ambiguity attitudes under various market conditions.Design/methodology/approachAmbiguity is measured as the volatility of return probability distributions extracted from high frequency intraday data via a method developed by Brenner and Izhakian (2018). The impact of ambiguity is then tested on stock market returns.FindingsThe results show that ambiguity is a priced factor in Turkish stock market with a positive premium that is distinct from risk premium. In contrast with the findings in the US market, the investors in Turkey show an increasing level of ambiguity aversion as expected probability of favorable returns deviate from the mean value. The investors are effectively ambiguity neutral in lateral markets. The results are robust to testing with higher moments, sentiment measures and under recession conditions.Originality/valueThis study contributes to empirically documenting ambiguity and ambiguity aversion in a major emerging market along with the opportunity to observe international differences in ambiguity attitudes.

Market-Based Probability of Stock Returns

Market-Based Probability of Stock Returns

Bank stocks, risk factors, and tail behavior

We examine how the tail behavior of risk factors affects the tail behavior of individual bank stock returns in the United States. Using 26 common risk factors, we construct univariate and multivariate conditional exceedance measures. We find that returns on banking industry, security-trading industry, and broad market portfolios have the largest impact on the probability of observing high positive tail returns on bank stocks. A small-minus-big bank return factor, market volatility, and a profitability risk factor have the largest impacts on the probability of lower tail returns. Bank capital ratios and total allowances for loan losses are notably related to tail risk. • The tail risk of bank returns is highly sensitive to the tail risk of several underlying risk factors. • Tail risk can more than triple when key market risk factors exhibit extreme realizations. • Bank capital ratios and total allowances for loan losses are also notably related to tail risk.

Can the probability of extreme returns be the basis for profitable portfolios? Evidence from China

Based on the generalized logit predicting model from Jang and Kang (2019), this paper estimates the ex-ante probability of extreme returns and finds that the significantly negative (positive) influence of the predicted crash (jackpot) probability is robust, whether based on the traditional portfolio construction, orthogonalized portfolio construction and Fame-Macbeth cross-section regression. Further analyses show both the behavioral speculators' trading and rational investors' arbitrage limits could be the sources of mispricing caused by extreme returns. Overall, this paper applies a predicting model to estimate the probabilities of the future extreme returns, and figures out the significant influence and possible sources of the crash and jackpot probabilities in China. Portfolios based on extreme return probabilities can be profitable and steady bases for uninformed investors in the Chinese stock market.

Modelo de decisión para determinar fuentes de retorno, incentivos y capacidad en un sistema de remanufactura

This research proposes a decision model to manage the purchase of new and used components in a re-manufacturing system. New components are purchased from a supplier who offers discounts based on the reserved capacity. The used components comes from various sources, where the quantity to be received from each source depends on the incentive provided. The model considers two stochastic elements: the return level per source (for used components) and the possibility that the shipment of new components is not received. A sensitivity analysis is performed based on a numerical example. The results demonstrate significant relationships between the costs to operate the return sources, the probability of low returns, and the incentive levels assigned to each source. The experiments also indicate how the incentives and the reserved capacity depends on the probability that the new components are not received.

Dividend policy and probability of extreme returns

PurposeThis study examines the effect of dividend policy on the ex ante probability of stock price crash and the ex ante probability stock price jump.Design/methodology/approachWe use the data of publicly listed non-financial firms from France and the ex ante measures of crash and jump probabilities (based on the Flexible Quadrants Copulas) to test our hypothesis during the period between 1997 and 2019.FindingsOur results show that dividend payments are negatively associated with the ex ante probability of crash and positively associated with the ex ante probability of jump. Our results are robust across various sub-samples and across different proxies of dividend policy. Our findings also hold when we use ex-post measures of crash and jump probabilities.Originality/valueUnlike prior literature, we use ex ante measures of crash and jump probabilities. The main advantage of this forward looking measure is that it allows for more flexibility by modeling the dependence between market returns and stock returns as functions of their actual state. Our measure is also consistent with the behavior of investors and market participants in a way that the market participants do not know the future outcome with certainty, but rather they are anticipating the future.

FRTB and Fat Tails

We calculate the probability of returns exceeding a multiple of Expected Shortfall for fat-tailed portfolios. Our results show that, taken in isolation, the Basel 3 FRTB Market Risk capital requirements are insufficient to prevent a large number of insolvencies. The impact of applying a cutoff to the tail distribution is also examined.

The effect of nitrogen-fertilizer and optimal plant population on the profitability of maize plots in the Wami River sub-basin, Tanzania: A bio-economic simulation approach

Maize (Zea mays L.) is the essential staple in sub-Saharan Africa (SSA) and Tanzania in particular; the crop accounts for over 30% of the food production, 20% of the agricultural gross domestic product (GDP) and over 75% of the cereal consumption. Maize is grown under a higher risk of failure due to the over-dependence rain-fed farming system resulting in low income and food insecurity among maize-based farmers. However, many practices, including conservation agriculture, soil and water conservation, resilient crop varieties, and soil fertility management, are suggested to increase cereal productivity in Tanzania. Improving planting density, and the use of fertilizers are the immediate options recommended by Tanzania's government. In this paper, we evaluate the economic feasibility of the improved planting density (optimized plant population) and N-fertilizer crop management practices on maize net returns in semi-arid and sub-humid agro-ecological zones in the Wami River sub-Basin, Tanzania. We introduce a bio-economic simulation model using Monte Carlo simulation procedures to evaluate the economic viability of risky crop management practices so that the decision-maker can make better management decisions. The study utilizes maize yield data sets from two biophysical cropping system models, namely the APSIM and DSSAT. A total of 83 plots for the semi-arid and 85 plots for the sub-humid agro-ecological zones consisted of this analysis. The crop management practices under study comprise the application of 40 kg N-fertilizer/ha and plant population of 3.3 plants/m2. The study finds that the use of improved plant population had the lowest annual net return with fertilizer application fetching the highest return. The two crop models demonstrated a zero probability of negative net returns for farms using fertilizer rates of 40 kg N/ha except for DSSAT, which observed a small probability (0.4%) in the sub-humid area. The optimized plant population presented 16.4% to 26.6% probability of negatives net returns for semi-arid and 14.6% to 30.2% probability of negative net returns for sub-humid zones. The results suggest that the application of fertilizer practices reduces the risks associated with the mean returns, but increasing the plant population has a high probability of economic failure, particularly in the sub-humid zone. Maize sub-sector in Tanzania is projected to continue experiencing a significant decrease in yields and net returns, but there is a high chance that it will be better-off if proper alternatives are employed. Similar studies are needed to explore the potential of interventions highlighted in the ACRP for better decision-making.

An Economic Comparison between Alternative Rice Farming Systems in Tanzania Using a Monte Carlo Simulation Approach

Tanzania is the second-largest producer of rice (Oryza sativa) in Eastern, Central, and Southern Africa after Madagascar. Unfortunately, the sector has been performing poorly due to many constraints, including poor agricultural practices and climate variability. In addressing the challenge, the government is making substantial investments to speed the agriculture transformation into a more modernized, commercial, and highly productive and profitable sector. Our objective was to apply a Monte Carlo simulation approach to assess the economic feasibility of alternative rice farming systems operating in Tanzania while considering risk analysis for decision-makers with different risk preferences to make better management decisions. The rice farming systems in this study comprise rice farms using traditional practices and those using some or all of the recommended system of rice intensification (SRI) practices. The overall results show 2% and zero probability of net cash income (NCI) being negative for partial and full SRI adopters, respectively. Meanwhile, farmers using local and improved seeds have 66% and 60% probability of NCI being negative, correspondingly. Rice farms which applied fertilizers in addition to improved seeds have a 21% probability of negative returns. Additionally, net income for rice farms using local seeds was slightly worthwhile when the transaction made during the harvesting period compared to farms applied improved varieties due to a relatively high price for local seeds. These results help to inform policymakers and agencies promoting food security and eradication of poverty on the benefits of encouraging improved rice farming practices in the country. Despite climate variability, in Tanzania, it is still possible for rice farmers to increase food production and income through the application of improved technologies, particularly SRI management practices, which have shown a promising future.

Assessing the Potential of Improving Livelihoods and Creating Sustainable Socio-Economic Circumstances for Rural Communities in Upper Egypt

Agriculture in Upper Egypt is characterized by unsustainable farming practices and a lack of the use of market intelligence. Improving agriculture in Upper Egypt is necessary, as farmers in this region need to meet the quality standards required by international markets if they are to increase export volumes. For this reason, agricultural interventions are required to transition from the current traditional farming systems and marketing practices to sustainable conservation farming practices and the use of market intelligence and logistics. The main aim of this research study is to assess the potential for improving the livelihoods of rural communities by conducting a cost–benefit analysis (CBA) and conducting a risk analysis using the Monte Carlo simulation method for the proposed agricultural interventions. Our results imply that the analyzed interventions are viable from both a financial and socio-economic point of view. The impacts of the interventions reveal real incremental employment opportunities at the farming level. In addition, the interventions have a very low probability of negative returns and become almost zero when we add the economic benefit to society.