•We use technical indicators computed from historical prices to predict stock price movements.•The effect of choosing different values of the time frame for computing technical indicators called ...window size is examined.•We investigate how the performance of a machine-learning predictive system depends on a forecast horizon and a window size.•The novel pattern is revealed: the highest prediction performance is reached when the window size is equal to the horizon.•Several performance metrics are used: prediction accuracy, winning rate, return per trade and Sharpe ratio.
The creation of a predictive system that correctly forecasts future changes of a stock price is crucial for investment management and algorithmic trading. The use of technical analysis for financial forecasting has been successfully employed by many researchers. Input window length is a time frame parameter required to be set when calculating many technical indicators. This study explores how the performance of the predictive system depends on a combination of a forecast horizon and an input window length for forecasting variable horizons. Technical indicators are used as input features for machine learning algorithms to forecast future directions of stock price movements. The dataset consists of ten years daily price time series for fifty stocks. The highest prediction performance is observed when the input window length is approximately equal to the forecast horizon. This novel pattern is studied using multiple performance metrics: prediction accuracy, winning rate, return per trade and Sharpe ratio.
This paper examines whether the human capital of first-time venture capital fund management teams can predict fund performance and finds that it can. I find that fund management teams with more ...task-specific human capital, as measured by more managers having past experience as venture capitalists and by more managers having past experience as executives at start-up companies, manage funds with greater fractions of portfolio company exits. I also find that fund management teams with more industry-specific human capital in strategy and management consulting and, to a lesser extent, engineering and non-venture finance manage funds with greater fractions of portfolio company exits. Perhaps counter-intuitively, I find that fund management teams that have more general human capital in business administration, as measured by more managers having MBAs, manage funds with lower fractions of portfolio company exits. Overall, measures of task- and industry-specific human capital are stronger predictors of fund performance than are measures of general human capital.
The behavioral agency model suggests that to preserve socioemotional wealth, loss-averse family firms usually invest less in R&D than nonfamily firms. However, behavioral agency model predictions are ...inconsistent with the well-accepted premise that family firms have a long-term investment orientation. We reconcile these seemingly incompatible predictions by adding insights from the myopic loss aversion framework, which deals with the impact of decision-making time horizons. The combination of these two prospect theory derivatives led us to hypothesize that family firms usually invest less in R&D than nonfamily firms but the variability of their investments will be greater owing to differences in the compatibility of long- and short-term family goals with the economic goals of a firm. However, when performance is below aspiration levels, we theorize that family goals and economic goals tend to converge. In this situation, the R&D investments of family firms are expected to increase and the variability of those investments decrease, relative to nonfamily firms. Analysis of 964 publicly held family and nonfamily firms from the Standard & Poor's 1500 between 1998 and 2007 support our hypotheses, confirming a need to take the heterogeneity of family firms more fully into account.
The behavioral agency model suggests family firms invest less in R&D than nonfamily firms to protect their socioemotional wealth. Studies support this contention but do not explain how family firms ...make R&D investments. We hypothesize that when performance exceeds aspirations, family firms manage socioemotional and economic objectives by making exploitative R&D investments that lead to more reliable and less risky sales levels. However, performance below aspirations leads to exploratory R&D investments that result in potentially higher but less reliable sales levels. Using a risk abatement model, our analyses of 847 firms over 10years supports our hypotheses.
We investigate whether business ties with portfolio firms influence mutual funds' proxy voting using a comprehensive data set spanning 2003 to 2011. In contrast to prior literature, we find that ...business ties significantly influence promanagement voting at the level of individual pairs of fund families and firms after controlling for Institutional Shareholder Services (ISS) recommendations and holdings. The association is significant only for shareholder-sponsored proposals and stronger for those that pass or fail by relatively narrow margins. Our findings are consistent with a demanddriven model of biased voting in which company managers use existing business ties with funds to influence how they vote.
•Optimum design and economic analysis of HRES for rural electrification in Korkadu.•RES are solar PV, wind turbine and bio-diesel generators.•Estimates the load forecasting for selected district in ...the Pre- HOMER analysis.•Desired HRES has to meet forecasted load demand for reliable electrification.
This paper validates the optimal design and techno-economic feasibility of hybrid renewable energy system (HRES) for rural area electrifying applications. Plan to a design of improved performance electrification system through village owned renewable resources, such as solar irradiations, wind speed and bio mass etc. The selected HRE system has to meet out electrical needs in optimum performance manner. Hear conducted a case study on remote village Korkadu is located in Union Territory of Pondicherry, India. The expected village demand of 179.32 Kwh/day and peak of 19.56 Kw was met with proposed HRE structure, which is consists of solar PV array, wind turbine, Bio mass power generator and Battery backup system in effectively. Load growth of the village was predicted through artificial neural network (ANN-BP) feed-back propagation and Levenberg-Marguardt (LM) data training optimum technique. Encounter the optimum performance of different HRE configuration was evaluated over by HOMER software. System’s economic dispatch was analysed through various dispatch strategy and come across the proposing companied dispatch strategy has more economical and performance benefits as total NPC of system as INR 1.21 million, one unit energy generation cost as INR 13.71 and annual battery throughput as 36.648 KWh/yr. This study also expresses the comparison analysis between proposed HRES structure performance with basic utility grid extension. The consequence of the proposed work shows the HRES in remote location can be a cost effective solution for sustainable development of rural regions.
Public investment projects must be linked to the priorities of Cohesion Policy and the Sustainable Development Strategy, highlighting the importance of the investment process and the allocation of ...funding sources. Therefore, steps such as the evaluation, selection and prioritization of public investment projects are key elements in the public investment process, in order to create a stronger and more sustainable future in the face of crises. This paper aims to clarify some issues related to the current challenges of allocating financial resources for public investment in the context of global economic crises. Public investment management makes the difference in making quality public investment and progress in achieving sustainable development goals. However, the present reality shows that ensuring the efficient management of public investment projects will always encounter difficulties, of which the most threatening are considered to be represented by global economic crises and more recently by the COVID 19 pandemic crisis, through their widespread effects on economies. Therefore, strengthening public investment management must be a priority in public investment policies to maximize the impact of available public resources.
Financial economics and corporate governance have long focused on the agency problems between corporate managers and shareholders that result from the dispersion of ownership in large publicly traded ...corporations. In this paper, we focus on how the rise of institutional investors over the past several decades has transformed the corporate landscape and, in turn, the governance problems of the modern corporation. The rise of institutional investors has led to increased concentration of equity ownership, with most public corporations now having a substantial proportion of their shares held by a small number of institutional investors. At the same time, these institutions are controlled by investment managers, which have their own agency problems vis-à-vis their own beneficial investors. We develop an analytical framework for understanding the agency problems of institutional investors, and apply it to examine the agency problems and behavior of several key types of investment managers, including those that manage mutual funds—both index funds and actively managed funds—and activist hedge funds. We show that index funds have especially poor incentives to engage in stewardship activities that could improve governance and increase value. Activist hedge funds have substantially better incentives than managers of index funds or active mutual funds. While their activities may partially compensate, we show that they do not provide a complete solution for the agency problems of other institutional investors.
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