scholarly journals Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 801
Author(s):  
Dhaval Adjodah ◽  
Yan Leng ◽  
Shi Kai Chong ◽  
P. M. Krafft ◽  
Esteban Moro ◽  
...  
Keyword(s):  
Social Learning ◽  
Implied Volatility ◽  
Pareto Frontier ◽  
Financial Assets ◽  
Market Uncertainty ◽  
Critical Question ◽  
Trade Off ◽  
Bayesian Models Of Cognition ◽  
Collective Information ◽  
Update Process

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.

scholarly journals Individual exploration and selective social learning: Balancing exploration-exploitation trade-offs in collective foraging

2021 ◽  
Author(s):  
Ketika Garg ◽  
Christopher T. Kello ◽  
Paul E Smaldino
Keyword(s):  
Social Learning ◽  
Social Information ◽  
Social Cues ◽  
Optimal Combination ◽  
Trade Off ◽  
Trade Offs ◽  
Lévy Walks ◽  
Power Law Exponent ◽  
Collective Foraging ◽  
Exploration Exploitation

Search requires balancing exploring for more options and exploiting the ones previously found. Individuals foraging in a group face another trade-off: whether to engage in social learning to exploit the solutions found by others or to solitarily search for unexplored solutions. Social learning can decrease the costs of finding new resources, but excessive social learning can decrease the exploration for new solutions. We study how these two trade-offs interact to influence search efficiency in a model of collective foraging under conditions of varying resource abundance, resource density, and group size. We modeled individual search strategies as Lévy walks, where a power-law exponent (μ) controlled the trade-off between exploitative and explorative movements in individual search. We modulated the trade-off between individual search and social learning using a selectivity parameter that determined how agents responded to social cues in terms of distance and likely opportunity costs. Our results show that social learning is favored in rich and clustered environments, but also that the benefits of exploiting social information are maximized by engaging in high levels of individual exploration. We show that selective use of social information can modulate the disadvantages of excessive social learning, especially in larger groups and with limited individual exploration. Finally, we found that the optimal combination of individual exploration and social learning gave rise to trajectories with μ ≈ 2 and provide support for the general optimality such patterns in search. Our work sheds light on the interplay between individual search and social learning, and has broader implications for collective search and problem-solving.

Regime Shifts in the Stock–Bond Relation in Australia

2007 ◽  
Vol 10 (01) ◽  
pp. 81-99 ◽  
Author(s):  
Garry Hobbes ◽  
Frewen Lam ◽  
Geoffrey F. Loudon
Keyword(s):  
Regime Switching ◽  
Implied Volatility ◽  
Methodological Approach ◽  
Market Uncertainty ◽  
Data Set ◽  
Transition Dynamics ◽  
State Dependent ◽  
Bond Returns ◽  
Bond Return ◽  
Equity Index

Previous evidence suggests that the implied volatility from equity index options, as a measure of stock market uncertainty, can provide "forward-looking information" about the stock–bond return correlation. This paper uses an alternative regime-switching autoregressive model to characterize state-dependent stock–bond return comovement and to evaluate the contribution of implied volatility in understanding transition dynamics. We confirm that implied volatility provides information about transition dynamics which is not inherent in the stock and bond returns, notwithstanding several different features of our data set and methodological approach.

scholarly journals Specialization and selective social attention establishes the balance between individual and social learning

2021 ◽  
Author(s):  
Charley M. Wu ◽  
Mark K. Ho ◽  
Benjamin Kahl ◽  
Christina Leuker ◽  
Björn Meder ◽  
...  
Keyword(s):  
Social Learning ◽  
Social Interactions ◽  
Field Data ◽  
Field Of View ◽  
Game Engine ◽  
Foraging Patterns ◽  
Trade Off ◽  
Collective Foraging ◽  
Simulation Results ◽  
Spatial Trajectories

AbstractA key question individuals face in any social learning environment is when to innovate alone and when to imitate others. Previous simulation results have found that the best performing groups exhibit an intermediate balance, yet it is still largely unknown how individuals collectively negotiate this balance. We use an immersive collective foraging experiment, implemented in the Minecraft game engine, facilitating unprecedented access to spatial trajectories and visual field data. The virtual environment imposes a limited field of view, creating a natural trade-off between allocating visual attention towards individual innovation or to look towards peers for social imitation. By analyzing foraging patterns, social interactions (visual and spatial), and social influence, we shine new light on how groups collectively adapt to the fluctuating demands of the environment through specialization and selective imitation, rather than homogeneity and indiscriminate copying of others.

scholarly journals Solving Portfolio Optimization Problems Using MOEA/D and Lévy Flight

2020 ◽  
Vol 12 (03n04) ◽  
pp. 2050005
Author(s):  
Yifan He ◽  
Claus Aranha
Keyword(s):  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problems ◽  
Statistical Test ◽  
Financial Assets ◽  
Lévy Flight ◽  
Nsga Ii ◽  
Levy Flight ◽  
Trade Off ◽  
Multi Objective

Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk. To solve this problem, recent studies applied multi-objective evolutionary algorithms (MOEAs) for its natural bi-objective structure. This paper presents a method injecting a distribution-based mutation method named Lévy Flight into a decomposition based MOEA named MOEA/D. The proposed algorithm is compared with three MOEA/D-like algorithms, NSGA-II, and other distribution-based mutation methods on five portfolio optimization benchmarks sized from 31 to 225 in OR library without constraints, assessing with six metrics. Numerical results and statistical test indicate that this method can outperform comparison methods in most cases. We analyze how Lévy Flight contributes to this improvement by promoting global search early in the optimization. We explain this improvement by considering the interaction between mutation method and the property of the problem. We additionally show that our method perform well with a round-lot constraint on Nikkei.

Redistribution Through Markets

Econometrica ◽  
2021 ◽  
Vol 89 (4) ◽  
pp. 1665-1698 ◽  
Author(s):  
Piotr Dworczak ◽  
Scott Duke Kominers ◽  
Mohammad Akbarpour
Keyword(s):  
Private Information ◽  
Competitive Equilibrium ◽  
Pareto Frontier ◽  
Allocative Efficiency ◽  
Market Clearing ◽  
Trade Off ◽  
Price Controls ◽  
Equilibrium Allocation ◽  
Lump Sum ◽  
Indivisible Object

Policymakers frequently use price regulations as a response to inequality in the markets they control. In this paper, we examine the optimal structure of such policies from the perspective of mechanism design. We study a buyer‐seller market in which agents have private information about both their valuations for an indivisible object and their marginal utilities for money. The planner seeks a mechanism that maximizes agents' total utilities, subject to incentive and market‐clearing constraints. We uncover the constrained Pareto frontier by identifying the optimal trade‐off between allocative efficiency and redistribution. We find that competitive‐equilibrium allocation is not always optimal. Instead, when there is inequality across sides of the market, the optimal design uses a tax‐like mechanism, introducing a wedge between the buyer and seller prices, and redistributing the resulting surplus to the poorer side of the market via lump‐sum payments. When there is significant same‐side inequality that can be uncovered by market behavior, it may be optimal to impose price controls even though doing so induces rationing.

Trade-Off Analysis to Solve a Probabilistic Multi-Objective Problem for Passive Filtering System Planning

2013 ◽  
Vol 14 (3) ◽  
pp. 275-284 ◽  
Author(s):  
Guido Carpinelli ◽  
Gabriella Ferruzzi ◽  
Angela Russo
Keyword(s):  
Information And Communication Technologies ◽  
Distribution System ◽  
Pareto Frontier ◽  
Inequality Constraints ◽  
Distribution Networks ◽  
Test System ◽  
Planning Problem ◽  
Trade Off ◽  
Simplified Approach ◽  
Multi Objective

Abstract In recent years, modern distribution networks have rapidly evolved toward complex systems due to the increasing level of penetration of distributed generation units, storage systems, and information and communication technologies. In this framework, power quality disturbances such as waveform distortions should be minimized to guarantee optimal system behavior. This article formulates the planning problem of passive filtering systems in a multi-convertor electrical distribution system as a probabilistic multi-objective optimization problem whose input random variables are characterized with probability density functions. A heuristic simplified approach including trade-off analysis issues is applied to solve the planning problem with the aim of optimizing several objectives and meeting proper probabilistic equality and inequality constraints. The approach is able to quickly find solutions on the Pareto frontier that can help the decision-maker to select the final planning alternative for practical operation. The proposed approach is applied to a 17-busbar distribution test system to evidence its effectiveness.

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