Enhances thematic investing with stock embeddings from textual data.
problem Challenges in constructing thematic portfolios due to overlapping sector boundaries and evolving market dynamics.
method Introduces THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning, aligning themes and stocks using their hierarchical relationship and incorporating stock returns.
result Theme-aligned portfolios demonstrate compelling performance, significantly outperforming large language models in thematic asset retrieval.
FinDKG uses LLMs to detect financial trends from news articles.
problem Detecting global financial trends from unstructured text data.
method Fine-tuned LLMs for generating DKGs, KGTransformer for analysis.
result KGTransformer outperforms existing thematic ETFs in financial thematic investing.
This paper optimizes portfolios of thematic sector stocks using LSTM models.
problem Designing an optimized portfolio of stocks to maximize return and minimize risk.
method Extracted stock prices from Jan 2016 to Dec 2020, used LSTM model for prediction, designed portfolios based on critical stocks.
result LSTM model accurately predicted future stock returns, indicating high accuracy.
Develops a new framework for integrating satellite allocations in small portfolios.
problem Feasibility constraints in small portfolios, not return predictability, are the primary concerns.
method A four-layer feasibility framework: physical, economic, structural, and epistemic.
result Closed-form feasibility bounds on satellite size, turnover, and breadth without return forecasts.
The present article surveys some mathematical aspects of the BCOV holomorphic anomaly equations introduced by Bershadsky, Cecotti, Ooguri and Vafa. It grew from a series of lectures the authors gave at the Fields Institute in the Thematic Program of Calabi-Yau Varieties in the fall of 2013.
The study analyzes ETFs' portfolio optimization and tail-risk management.
problem Analyzing the performance of actively managed ETFs in managing risk and diversification.
method Daily Bloomberg data for 30 funds, evaluating various strategies under long-only and long-short constraints.
result Tangency-type portfolios generally outperform buy-and-hold benchmarks, while minimum-variance and CVaR-minimizing portfolios sacrifice upside for downside control.
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary va…
The paper develops a decision support system for hierarchical text classification of conference proceedings.
problem Classifying documents with a fixed hierarchical structure of topics.
method Developed a weighted hierarchical similarity function to calculate topic relevance, using entropy of words to estimate weights.
result The weighted hierarchical similarity function improves ranking accuracy compared to other methods.
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, …
Develops MIS, a probabilistic model for multi-industry classification.
problem GICS's limitation of assigning each firm to exactly one industry, especially for diversified firms.
method Topic modeling to probabilistically assign firms to multiple industries based on business descriptions.
result Demonstrates MIS's ability to flexibly assign firms to multiple industries with relevance probabilities.
Survey on twisted dynamical zeta functions and Fried's conjecture.
problem Analyzing twisted dynamical zeta functions and their relation to Fried's conjecture.
method Review of existing literature and mini-course presentation.
result Discussion and validation of Fried's conjecture.
Lecture notes on BGG complexes using Lie groups and algebras.
problem Constructing BGG complexes on open domains.
method Representation theory of semisimple Lie groups and Lie algebras.
result Introduction of BGG complexes with Lie group and algebra insights.
Machine learning detects and classifies cognitive distortions in mental health texts.
problem Detect and classify cognitive distortions in mental health texts to improve treatment.
method Machine learning framework using crowdsourced and therapy dataset.
result Model achieved high F1 scores for detecting and classifying cognitive distortions.
Rdimtools simplifies DR and IDE for high-dimensional data analysis.
problem Discovering patterns in complex high-dimensional data.
method Provides an R package with 133 DR and 17 IDE algorithms.
result Facilitates geometric understanding of high-dimensional data.
New AI stock indices classify firms' AI engagement using 10-K filings.
problem Opaque AI selection criteria in existing ETFs.
method NLP analysis of 10-K filings to classify AI stocks.
result Companies with higher AI engagement have greater positive returns.
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, …
The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning, and models in big data was held in 2015 i…
Study of public and private VC relationships in France using qualitative methods.
problem Understanding interactions between public and private venture capitalists in France.
method Qualitative approach with semi-structured interviews and thematic content analysis.
result Formal or informal relationships between public and private VCs are a 'economico-cognitive' approach to networking and innovation.
Corporate bond factor research is flawed due to measurement errors and ex-post filtering.
problem Replication crisis in corporate bond factor research.
method Analysis of 108 signals across nine thematic clusters, correction of transaction prices and return filtering.
result Majority of previously documented factors do not produce statistically significant alphas after correction.
Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge of the entire corpus, and second, a focused search through the corpus specifically to find such u…
LDTA expands LDA's topic modeling capacity with tree-structured priors.
problem Limited expressiveness of Dirichlet priors in LDA for complex topic relationships.
method Introduces Latent Dirichlet-Tree Allocation (LDTA) with Dirichlet-Tree (DT) priors, and develops universal mean-field variational inference and Expectation Propagation.
result LDTA enables expressive, tree-structured priors over topic proportions, expanding modeling capacity of LDA.
In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is i…
The paper clarifies long-horizon investment and DCA, showing no risk reduction but different exposure profiles.
problem Misleading claims about reducing risk with longer investment horizons and DCA.
method Unified probabilistic framework, defining risk and uncertainty, and introducing effective investment exposure.
result Different investment timing strategies can lead to distinct exposure profiles over time, affecting risk and uncertainty.
ChatGPT scores corporate investment plans, predicting future spending and returns.
problem Measuring and predicting corporate investment plans.
method Created a firm-level ChatGPT investment score based on conference calls.
result The investment score predicts future capital expenditures and returns.
Investment herding can reduce household consumption, a phenomenon called crowding-out effect.
problem Investment herding's impact on household consumption.
method Optimal control theory to model and solve for household investment and consumption decisions.
result Existence of crowding-out effect due to investment herding.
Qlib aims to integrate AI into quantitative investment.
problem Challenges in applying AI to quantitative investment.
method Design and develop Qlib to accommodate AI-driven workflow.
result Qlib realizes the potential of AI technologies in quantitative investment.
AI enhances quantitative investment for better returns and risk control.
problem Achieving stable returns through AI in quantitative investment.
method Application of AI technology in quantitative investment strategies.
result AI improves investment performance and risk management.
The investment economy is a main characteristic of prosperous society. The investment portfolio management is a main financial problem, which has to be solved by the investment, commercial and central banks with the application of modern portfolio theory in the investment economy. We use the learning analytics together…
We study optimal investment problems under the framework of cumulative prospect theory (CPT). A CPT investor makes investment decisions in a single-period financial market with transaction costs. The objective is to seek the optimal investment strategy that maximizes the prospect value of the investor's final wealth. W…
This paper proposes an embedding-based neural network for more accurate investment return prediction.
problem Accurately predicting investment returns requires understanding industry knowledge and news, as well as leveraging relevant theories.
method The approach uses embedding to encode investment IDs into low-dimensional vectors, leveraging dual branches to separate different information, and employs the swish activation function.
result The proposed embedding-based dual branch model outperforms traditional machine learning models like Xgboost, Lightgbm, and Catboost on the Ubiquant Market Prediction dataset.
New approach to goal-based investing using hedging and reinforcement learning.
problem Maximizing probability of reaching investment goals with varying risk aversion.
method Lower partial moments, quantile hedging, efficient hedging, reinforcement learning.
result Optimal investment policies for goal-based investing are equivalent.
Investment decision triggered by a convex curve in a two-factor uncertainty model.
problem Optimal irreversible investment in a company with two products whose prices follow geometric Brownian motions.
method Two-dimensional optimal stopping problem, nonlinear integral equation, convex curve characterization.
result Optimal investment decision is characterized by a convex curve, unique solution to a nonlinear integral equation.
Geometric structure reveals optimal investment and hedging products.
problem Optimal design of investment and hedging products.
method Investigation of geometric structure in risks and returns using a simple formula.
result Duality between hedging and investment with geometric interpretation of rationality.
Intangible investment becomes a strong predictor of stock returns over time.
problem Understanding the role of intangible investment in stock returns over different periods.
method Comparing intangible investment's predictive power over two distinct periods (1963-1992 and 1993-2022) using orthogonal factors.
result Intangible investment's predictive power for stock returns has significantly increased over time, becoming a main predictor for recent periods.
Study shows institutional investments significantly impact cryptocurrency market evolution.
problem Limited understanding of institutional investments' role in cryptocurrency market evolution.
method Quantitative analysis of 1324 cryptocurrencies' investments from 2014-2022.
result Institutional investments correlate with cryptocurrency market capitalization.
WSB community outperforms investment banks in stock picks.
problem Can WSB's community provide better investment advice than banks?
method Data-driven comparison of WSB and bank recommendations on S&P 500 stocks.
result WSB recommendations outperform banks in some cases and detect top stocks better.
In portfolio optimization problems, the minimum expected investment risk is not always smaller than the expected minimal investment risk. That is, using a well-known approach from operations research, it is possible to derive a strategy that minimizes the expected investment risk, but this strategy does not always resu…
This paper solves optimal consumption-investment choices with wealth-driven risk aversion using neural networks.
problem Optimal consumption-investment choices under wealth-driven risk aversion.
method Neural network LSTM trained on jump-diffusion model data to optimize investment rate and consumption.
result Neural network approach shows promising results in solving the investment problem.
Solves pair trading problem using consumption-investment theory.
problem Pair trading consumption-investment problem
method Reduces HJB equation to a linear parabolic equation solvable explicitly
result Simple solution to pair trading problem
Speed bumps reduce but do not fully eliminate investment in fast trading technology.
problem Limiting low-latency trading to curb investment in fast trading technology.
method Built an experimental trading platform to test the effects of speed bumps on investment in fast trading technology.
result Asymmetric speed bumps reduce investment in speed by only 20%, and increasing the magnitude further reduces investment by 8.33%. Symmetric speed bumps have no effect on investment levels.
We study an optimal investment control problem for an insurance company. The surplus process follows the Cramer-Lundberg process with perturbation of a Brownian motion. The company can invest its surplus into a risk free asset and a Black-Scholes risky asset. The optimization objective is to minimize the probability of…
Even in the face of deteriorating and highly volatile demand, firms often invest in, rather than discard, aging technologies. In order to study this phenomenon, we model the firm's profit stream as a Brownian motion with negative drift. At each point in time, the firm can continue operations, or it can stop and exit th…
Proposes an end-to-end deep learning framework for active investing.
problem Constructing an active investment portfolio via deep learning.
method End-to-end deep learning framework covering factor selection, combination, stock selection, and portfolio construction.
result Demonstrates effectiveness of E2E deep learning framework in active investing.
Proposes a new method to rank risky investments based on Omega measure.
problem Evaluating and ranking risky investment projects.
method Introduces an investment certainty equivalence approach and uses the Omega measure.
result Proposed method ranks projects differently from conventional risk-adjusted discount rate (RADR) approach.
Alpha-GPT 2.0 integrates human insights into AI-driven investment research.
problem Efficiency and precision in quantitative investment research.
method Iterative Human-AI interaction using large language models.
result Enhanced efficiency and precision in quantitative investment research.
ChatGPT selects stocks for investment portfolios, but optimization models improve results.
problem Using AI for investment advice due to model inaccuracies.
method Used ChatGPT to generate a stock universe, then compared various portfolio optimization strategies.
result Combining AI-generated stock selection with advanced optimization models yields better investment outcomes.
New method uses impact IRR to assess impact investments.
problem Determining financial returns of impact investments remains challenging.
method Adapts modern portfolio theory and financial tools to evaluate impact investments.
result Demonstrates the feasibility and utility of impact IRR for optimizing impact investments.
Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains a challenge in digital humanities. While most previous work focuses on "distant reading" by algorithmically discove…