An analytical approach for behavioral portfolio model with time discounting preference

2018 ◽  
Vol 52 (3) ◽  
pp. 691-712
Author(s):  
Guang Yang ◽  
Xinwang Liu ◽  
Jindong Qin ◽  
Ahmed Khan
Keyword(s):  
Portfolio Selection ◽  
Investment Strategy ◽  
Cumulative Prospect Theory ◽  
Investment Strategies ◽  
Short Term ◽  
Time Discounting ◽  
Present Bias ◽  
Proposed Model ◽  
Portfolio Selection Model

This paper presents a behavioral portfolio selection model with time discounting preference. Firstly, we discuss the portfolio selection problem and then modify this model based on cumulative prospect theory (CPT) as well as considering investors’ time discounting preference in psychology. Furthermore, an analytical solution with satisfying behavior is given for our proposed model, the results show that when investors’ goals are very ambitious, they put a high proportion of their wealth in long-term goals and adopt aggressive investment strategies with high leverage to reach short-term goals and the overall investment strategy also displays high leverage. Finally, numerical analysis is given and it is shown that investor who tends to future bias performs adequate confidence and patience whereas investor with present bias is apt to the immediate interests.

Is This the End of the ‘Long Term’ for Pensions Actuaries?. A Discussion Meeting

2006 ◽  
Vol 12 (1) ◽  
pp. 63-78
Keyword(s):  
Investment Strategy ◽  
Pension Scheme ◽  
Investment Strategies ◽  
Short Term ◽  
Retirement Benefits ◽  
The Government ◽  
To Come ◽  
Legislative Changes ◽  
Funding Environment

INTRODUCTIONThe Profession claims to make financial sense of the future, and our particular angle is our purported ability to see past the whims of the short termists and keep an unwavering eye on the long term. The pensions arena has been no different … until now?In June 2003, the Government converted defined retirement benefits unambiguously from an arguably vague promise to a debt, behind which the sponsor has to stand. A series of subsequent legislative changes, including the introduction of Scheme Specific Funding and of the Pension Protection Fund (PPF), with its proposed risk-based levies, has forced trustees to take a more commercial view to make sure that accrued benefits are met.This stands in contrast to the gentler ‘funding’ environment in which pension schemes and Scheme Actuaries had become used to working. In that environment, the actuarial ‘long term’ justified many of the decisions taken in funding schemes — the long-term focus drove investment strategy and the approach to setting or agreeing contribution rates.Has the rationale for the ‘long term’ disappeared: now that some of the discussion about funding has included talk of deficit correction periods of less than five years; now that accounting standards put any investment and actuarial volatility in the pension scheme into the sponsor's accounts every year; and now that PPF levies will change from year to year as funding levels and sponsor covenants change?Has the Actuarial Profession over-reacted in focusing on the short term, or has it under-reacted? Will investment strategies look very different in years to come? Will valuations and funding advice take on a different shape?

scholarly journals Neural Architecture Search for a Highly Efficient Network with Random Skip Connections

2020 ◽  
Vol 10 (11) ◽  
pp. 3712
Author(s):  
Dongjing Shan ◽  
Xiongwei Zhang ◽  
Wenhua Shi ◽  
Li Li
Keyword(s):  
State Of The Art ◽  
Cell Structure ◽  
Frequency Discrimination ◽  
Search Space ◽  
Short Term ◽  
Cell Parameters ◽  
Neural Architecture ◽  
Proposed Model ◽  
Initialization Scheme

Regarding the sequence learning of neural networks, there exists a problem of how to capture long-term dependencies and alleviate the gradient vanishing phenomenon. To manage this problem, we proposed a neural network with random connections via a scheme of a neural architecture search. First, a dense network was designed and trained to construct a search space, and then another network was generated by random sampling in the space, whose skip connections could transmit information directly over multiple periods and capture long-term dependencies more efficiently. Moreover, we devised a novel cell structure that required less memory and computational power than the structures of long short-term memories (LSTMs), and finally, we performed a special initialization scheme on the cell parameters, which could permit unhindered gradient propagation on the time axis at the beginning of training. In the experiments, we evaluated four sequential tasks: adding, copying, frequency discrimination, and image classification; we also adopted several state-of-the-art methods for comparison. The experimental results demonstrated that our proposed model achieved the best performance.

scholarly journals An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 861 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Departure Time ◽  
Long Short Term Memory

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.

scholarly journals Long Short-Term Memory with Dynamic Skip Connections

2019 ◽  
Vol 33 ◽  
pp. 6481-6488 ◽  
Author(s):  
Tao Gui ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yaosong Lin ◽  
Minlong Peng ◽  
...  
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Training Data ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Transition Functions ◽  
Proposed Model ◽  
Long Short Term Memory

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.

scholarly journals Time Horizon, Costs of Equity Capital, and Generic Investment Strategies of Firms

2007 ◽  
Vol 20 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Thomas Zellweger
Keyword(s):  
Default Risk ◽  
Time Horizon ◽  
Family Firms ◽  
Economic Value ◽  
Equity Capital ◽  
Investment Strategies ◽  
Cost Of Equity Capital ◽  
Short Term ◽  
Investment Projects

Recent literature (McNulty, Yeh, Schulze, & Lubatkin, 2002) states that the assumptions behind the capital asset pricing model, in particular the irrelevance of time horizon, do not correspond to the characteristics of firms that prefer long-term investment horizons. I show that family firms display a longer time horizon than most of their nonfamily counterparts, since (1) family firms display a longer CEO tenure, (2) this type of firm strives for long-term independence and succession within the family, and (3) due to the fact that family firms are overrepresented on western European stock markets in cyclical industries in which business cycles inhibit short-term success. As the annual default risk of an investment diminishes with increasing holding period (Hull, 2003), the risk-equivalent cost of equity capital of firms with longer planning horizons (e.g., family firms) can be lower as well. Based on the assumption that economic value to shareholders is created when firms invest in projects with returns above the associated cost of capital (Copeland, Koller, & Murrin, 2000), I argue that long-term-oriented firms can tackle unique investment projects represented by two generic investment strategies—the perseverance and the outpacing strategy. The first one, the perseverance strategy, represents investment strategies in which long-term-oriented firms invest in lower return but equal risk projects than their more short-term-oriented counterparts. The second one, the outpacing strategy, comprises investment projects with higher risk and equal return than the short-term competitors.

scholarly journals Modeling sensitivity indices of industrial enterprise organizational change

2021 ◽  
Vol 10 (45) ◽  
pp. 230-241
Author(s):  
Victoriia Bilyk ◽  
Olena Kolomytseva ◽  
Olha Myshkovych ◽  
Nataliia Tymoshyk ◽  
Denis Shcherbatykh
Keyword(s):  
Organizational Change ◽  
Theory And Practice ◽  
Organizational Changes ◽  
Short Term ◽  
Industrial Enterprises ◽  
Sensitivity Indices ◽  
Proposed Model ◽  
Development Effectiveness ◽  
Made In

Evaluation of sensitivity of commercial enterprises to organizational changes should be made in terms of short-term planning for which it is important to ensure the financial results, as well as in terms of long-term planning, which is important for non-monetary indicators of development effectiveness. To solve this problem, the paper is designed model sensitivity Descriptive indicators of industrial enterprises to organizational changes, reflecting monetary and non-monetary effects of organizational change. The authors determined that the proposed model allows for the analysis of organizational change with regard to their impact on monetary and non-monetary efficiency. This paper contributes to the theory and practice at the border to ensure a balance between short-term and long-term development of industrial enterprises. Convincingly demonstrated the possibility of using research results in practice.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

scholarly journals Optimization Model for the Long-Term Operation of an Interprovincial Hydropower Plant Incorporating Peak Shaving Demands

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4804
Author(s):  
Rui Cao ◽  
Jianjian Shen ◽  
Chuntian Cheng ◽  
Jian Wang
Keyword(s):  
Power Generation ◽  
Optimization Model ◽  
Hydropower Plant ◽  
Mixed Integer ◽  
Short Term ◽  
Long Distance ◽  
Peak Shaving ◽  
Term Operation ◽  
Proposed Model

The increasing peak-to-valley load difference in China pose a challenge to long-distance and large-capacity hydropower transmission via high-voltage direct current (HVDC) lines. Considering the peak shaving demands of load centers, an optimization model that maximizes the expected power generation revenue is proposed here for the long-term operation of an interprovincial hydropower plant. A simulation-based method was utilized to explore the relationships between long-term power generation and short-term peak shaving revenue in the model. This method generated representative daily load scenarios via cluster analysis and approximated the real-time electricity price of each load profile with the time-of-use price strategy. A mixed-integer linear programming model with HVDC transmission constraints was then established to obtain moving average (MA) price curves that bridged two time-coupled operations. The MA price curves were finally incorporated into the long-term optimization model to determine monthly generation schedules, and the inflow uncertainty was addressed by discretized inflow scenarios. The proposed model was evaluated based on the operation of the Xiluodu hydropower system in China during the drawdown season. The results revealed a trade-off between long-term energy production and short-term peak shaving revenue, and they demonstrated the revenue potential of interprovincial hydropower transmission while meeting peak shaving demands. A comparison with other long-term optimization methods demonstrated the effectiveness and reliability of the proposed model in maximizing power generation revenue.

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