The increasing complexity of financial markets and the growing need for personalisation present significant challenges to the traditional investment advisory model. To address this issue, this paper puts forth a proposal for an investment advisor robot 2.0 system based on deep neural networks. This system is designed to offer bespoke investment recommendations aligned with the risk appetite, financial objectives, and market dynamics of individual investors. The proposed method employs a long short-term memory network (LSTM) to generate personalised investment strategies in real time through the analysis of historical market data, investor behaviour data and real-time financial information. The innovation lies in the introduction of a multi-objective optimisation mechanism, which automatically adjusts the allocation of the portfolio through deep learning algorithms, optimises the balance between risk and return, and significantly improves the accuracy and practicality of investment recommendations. The experimental results demonstrate that, in comparison to the conventional investment advisory model, the system exhibits enhanced resilience in responding to market volatility across a range of simulated scenarios. It is also evident that the system is capable of formulating more forward-thinking investment strategies, while simultaneously demonstrating heightened stability and yield.

This paper explores the potential and challenges of mobile Internet in household investment decisions. The rapid development of mobile Internet has brought opportunities and challenges to household asset allocation, especially in promoting greater participation in venture asset investment. This paper focuses on the application potential of machine learning in analyzing household investment behavior patterns and trends. It reveals the potential household income rules, consumption patterns and asset allocation preferences through extensive data analysis. However, machine learning faces many challenges, such as data privacy protection, algorithmic interpretation, and data acquisition costs. Finally, the paper calls for further research and exploration to deepen understanding of how technological innovation can drive intelligent and optimized household financial decisions.

When it comes to e-commerce and logistics industry developing rapidly, the location and path planning of green warehousing and logistics have attracted more and more attention, and how to improve efficiency and reduce environmental impact has become the focus of research. This paper presents a new optimization approach based on Deep Neural Network (DNN), which is used to solve the multi-objective optimization problem in the warehouse location and path planning. We propose to use a deep neural network model to optimize the weights and parameters of the network as a whole through the combined PSO method, so that the dynamic and diverse needs of the logistics environment can be better adapted to changes. Unlike traditional methods based on heuristics or simple machine learning models, this paper adopts the principle of minimizing the transportation cost, while achieving the minimization of energy consumption and the limited carbon emissions in path planning, i.e. optimizing the particle swarm of DNN. Empirical results indicate that, when compared to classical optimization techniques, our approach enhances transportation cost and environmental sustainability by 18% and 12%.

The labour market constitutes a pivotal element of the national economy, exerting a profound influence on the health and stability of the broader economic system. In recent years, the role of media journalism in the dissemination of information has increased significantly, with a notable impact on supply-demand and price fluctuations in the labour market. By analysing the impact of media news, it is possible to gain a deeper understanding of, and insight into, the dynamics of the labour market. This study employs a combination of machine learning techniques and a dynamic stochastic general equilibrium model (DMP model) to assess the influence of media news on labour market efficiency. The use of machine learning algorithms enables the processing and analysis of substantial quantities of media news data, facilitating the extraction of sentiment and topic changes. The DMP model is employed to simulate the matching process in the labour market and evaluate the impact of news on the efficiency of matching job seekers and job openings. The study demonstrates, through experimental analysis, the disparate impacts of media news on the labour market across different economic cycles, and validates the efficacy and practicality of the model.

The impact of financial risks extends beyond the normal operation and survival of industrial and commercial enterprises and financial institutions. They also have the potential to impede the stable development of a country's and even the world's financial economy. This is clearly demonstrated by the severe consequences of the frequent financial crises that have occurred in recent years. It thus follows that the prevention of financial risks has become one of the core tasks of the operation and management of industrial and commercial enterprises and financial institutions. Portfolio optimization techniques are employed in the construction of a variety of asset allocation models, with reinforcement learning algorithms used to dynamically adjust investment strategies with the objective of maximizing returns and minimizing risks. The preliminary construction of an asset allocation model is achieved through the utilization of a genetic algorithm. Genetic algorithms emulate the processes of natural selection and genetic variation to identify the optimal portfolio of assets that will yield the greatest returns at a pre-established level of risk. Subsequently, the Deep Q-Learning algorithm is introduced to facilitate dynamic adjustments and optimization of the asset allocation, based on the initial construction. Deep Q-learning employs deep neural networks to forecast the prospective returns of disparate investment strategies, thereby optimizing the decision-making process through continuous learning and updating. The combination of genetic algorithms and deep Q-learning enables the system to identify the optimal investment strategy under a diverse range of initial conditions and to adapt it in real-time to respond to market fluctuations and uncertainties. The experimental analysis demonstrates that the proposed method exhibits an exemplary capacity for risk control and a robust ability to generate stable income growth across diverse market environments. In simulation experiments, portfolios constructed using this method demonstrate lower volatility and higher average returns than those generated through traditional methods.

This paper proposes a novel, behaviorally grounded proxy for household financial sophistication by analyzing mortgage bunching behavior at the Federal Housing Finance Agency’s (FHFA) conforming loan limit (CLL). The CLL creates a sharp discontinuity in mortgage interest rates between conforming and jumbo loans, which rational, financially sophisticated borrowers should exploit by adjusting their loan amount to stay below the threshold. Using a national loan-level dataset from CoreLogic and adapting notch-based bunching estimation techniques, this paper measures the extent to which households respond to this incentive and interpret the normalized bunching mass as a revealed-preference indicator of financial sophistication. The paper validates this proxy by demonstrating that borrowers with higher FICO scores and those who refinance—commonly considered more sophisticated—exhibit significantly greater bunching behavior. Furthermore, it estimates the interest rate semi-elasticity of mortgage demand and control for liquidity constraints to isolate the role of information and optimization ability. This paper finds that costly suboptimal borrowing is widespread, with non-bunching behavior imposing substantial financial losses on households. The approach provides a scalable, data-driven framework for evaluating household decision-making in housing finance and has broad implications for FHFA policy design, consumer protection efforts, and the targeting of financial literacy initiatives.