# Exploring the Innovation Diffusion of Big Data Robo-Advisor
## 1 Introduction  
In recent years, with the rise of artificial intelligence (AI) algorithms, AI algorithms have been used in academia to develop predictive models for the stock market, ranging from early time series and logistic regression to recent decision-making trees. [PERSON]'s stock prediction model (2020) could also be used to speculate the pricing evolution of financial assets. Predictive models can be classified into linear and non-linear models: the former would include time series models, such as autoregressive integrated moving averages, and exponential smooth models, among others, while networks would be classified to the latter [1]. Given that stock returns are not stationary, and present long-term dependence, this second group of models have shown effectiveness in achieving a higher accuracy, and a reduction in errors, compared with linear models [2]. Existing research related to the prediction of stock prices and their trends mainly utilize artificial neuronal networks. Due to substantial advancements in computing power, neural networks based on AI have become mainstream in recent research. Recurrent neural networks (RNN) and convolutional neural networks (CNN) are among the mainstream models used for stock market forecasting, and substantial time and energy have been invested to develop stock market forecasting models with higher accuracy rates. Big data robo-advisors then use these artificial intelligence (AI) algorithm predictive models as the basis for investment decisions [1, 2]. For years, market investors have been pursuing a technique that beats the market, such as predicting the future direction of the stock market by studying the relationship between stock prices and volume, or exploring the value of a company, hoping to gain the opportunity to make a profit by understanding the difference between the value of a company and its market price. The existing method of predicting stock market pricescan be generally divided into two types: fundamental analysis and technical analysis. Fundamental analysis based on macroeconomic data to study the company's financial or revenue status as an investment judgment. Technical analysis is based on the theoretical basis of historical repetition, and the correlation between price and quantity shows the behavior of the market by taking advantage of the impact of past trading activities, and by analyzing the patterns and trends shown in the price and volume tables. In recent years, with technological improvements in computing powers, many novel algorithms have also emerged, as a series of stock market forecasting technologies have been developed, including time series or neural networks in hope to accurately predict the trend of stock prices by leveraging machine computing powers. The time series and neural network prediction models are based on historical repetition, and thus categorized as technical analysis [3]. Many factors are known to affect the stock market. Past studies in the financial investment field found that the stock market has the characteristics of a \"stochastic random walk\", and thus cannot be predicted easily. According to the efficient market hypothesis by [PERSON] (1965), asset prices reflect all available information. A direct implication is that it is impossible to \"beat the market\" consistently, on a risk-adjusted basis, since market prices are fully and quickly reflected, and only react to new information [4]. Three types of efficiencies exist depending on the efficiency of the market's response to information: strong, semi-strong, and weak. These categories of tests refer to the information set used in the statement \"prices reflect all available information.\" Weak-form tests study the information contained in historical prices. Semi-strong form tests serve to study information (beyond historical prices) which is publicly available, and strong-form tests serve to regard private information.