Everything is "Number": The Mathematical Philosophy and Human Nature in Big Data Analytics

In the field of big data analysis, how do we strike a balance between the objective nature of data and the subjective aspects of human factors? And in financial trading, how does this balance manifest in its impact on decision-making?

Dr. Lu Chen: In big data analysis, balancing the factors of data objectivity and human subjectivity is a challenging task. In the context of financial trading, this balance has a significant influence on decision-making, which is evident in the following aspects: 1. The financial market serves as a crucible where all factors, whether related to human nature or technology, are rigorously tested. While big data analysis can solve certain problems, it does not resolve every issue, particularly at higher levelswhere human intuition and luck come into play, thereby giving rise to the role of subjectivity. 2. Model risk is an inherent reality;even if backtesting results for a model are impeccable, changes in the macro environment, meso-environment, and micro-environment over time can render the model ineffective in actual trading scenarios. 3. Uncertainty and randomness act as double-edged swords in financial markets, presenting both opportunities and risks. The pervasive presence of these uncertainties complicates decision-making in the market, necessitating a delicate balance between data objectivity and human subjectivity. 4. There exists a distinction between the human perspective and an omniscient viewpoint in financial markets. Whilebig data analysis can offer some guidance, practical operations also heavily rely on human agency, including judgment, intuition,and even luck. 5. The case of renowned fund managers like Ge Lan and Cai Songsong, who achieved great success in the past but later withdrew from the market, suggests that they might have encountered challenges in balancing data objectivity and human subjectivity. This also illustrates that there is no absolute formula for guaranteed success in the financial markets.

As the most fundamental layer of causality, how can stochastic quantum mechanics be integrated with big data analysis? How does stochastic quantum mechanics explain randomness and uncertainty in data, and what implications does it havefor financial trading decisions?

Dr. Lu Chen : Regarding the integration of stochastic quantum mechanics, which serves as a fundamental cause-and-effect mechanism, with big data analysis, it essentially explores how to deal with randomness and uncertainty in data. Whether it's quantum mechanics, big data analysis, or financial trading, they all adhere to a basic principle: to peel away superficial phenomena and strive for the core essence of things—a thought process often referred to as the "principle of first causes."In this process, we leverage big data analysis to excavate deeper layers of information beneath the surface. For instance, in financial trading, we utilize data analysis to identify the underlying reasons hidden behind charts and graphs. This involves applying the principle of first causes to dig deeper into the intrinsic value of data. Crucially, during this process, human wisdom and human nature play a pivotal role. A classic example is Soros's decision to short the pound, where he personally attended a finance ministers' conference, observed their expressions, and inferred their true intentions, ultimately making a correct investment decision. Overall, the combination of quantum mechanics and big data analysis aims at pursuing a deeper level of understanding and analytical methods. This approach not only requires advanced scientific and technological means but also relies on human wisdom and courage. We should persistently explore and endeavor to discover the truths lying beneath the data.

In the field of data analysis for financial trading, how do intuition and inspiration play a role? Could intuition and inspiration possibly deviate from objective data, leading to inaccurate or misleading conclusions? Can intuition and inspiration be structured and systematized? Is it possible to establish a framework or methodology that integrates intuition and inspiration into the process of data analysis or AI decision-making to enhance the quality and depth of analysis and decision-making?

Dr. Lu Chen : In the data analysis for financial transactions, intuition and inspiration are highly significant. Intuition and inspiration can assist us in discovering deeper patterns and correlations during the data analysis process. However, intuition and inspiration are difficult to structure and systematize because they largely depend on personal experience and intuition, representing a different level altogether. This implies that we cannot guarantee that intuition and inspiration will always accurately reflect objective data. Indeed, sometimes, intuition and inspiration may diverge from objective data, resulting in inaccurate or misleading conclusions. Nonetheless, this does not mean we should entirely discard intuition and inspiration. On the contrary, we should strive to apply them in data analysis while exercising caution to avoid over-reliance. Although we cannot fully control intuition and inspiration, we can stimulate them by continually engaging with more data. Data can serve as a medium, triggering our intuition and inspiration, and help us uncover deeper patterns and relationships.