Algorithmic system for dynamic conditional asset pricing analysis and financial intelligence technology platform automation

Abstract

The current invention pertains to the novel, nonobvious, and applicable design and development of an algorithmic system for dynamic conditional asset pricing output and financial intelligence technology platform automation. Core technicality entails the consistent estimation of dynamic conditional alphas after one controls for myriad fundamental characteristics such as market risk, size, value, momentum, asset investment growth, and operating profitability through recursive multivariate filtration. Conditional specification test evidence supports the use of the dynamic conditional multifactor asset pricing model against the static alternatives. The fintech platform allows users to interact with one another by transmitting valuable units of financial intelligence and information in an online social network. The information units include dynamic conditional alpha rank order, key financial ratio summary, quadripartite visualization of financial data, and financial statement analysis. The fintech platform automates social network functions for better interactive engagement through minimum viable cloud computing facilities for web mobile app design.

With respect to the current invention, we claim: 
     
         1 . A dynamic conditional asset pricing system for the effective and accurate return prediction of a risky asset, wherein each risky asset is a stock, bond, currency, commodity, or a portfolio of risky assets, comprising:
 a database for collecting financial price and return data and financial statement data of the risky asset;   a Sharpe ratio generation module for yielding the risky-asset-specific ratio of average excess return to standard deviation of excess returns on the risky asset; and   a risky asset return prediction module for effective and accurate risky asset return prediction, dynamic conditional alpha rank order, and additional financial intelligence and information including major financial ratios and financial statements comprising balance sheets, income statements, and cash flow statements as well as mean excess returns and return volatilities.   
     
     
         2 . The dynamic conditional asset pricing system of  claim 1 , further comprising:
 a dynamic conditional multifactor asset pricing model; and   a recursive multivariate filtration for extracting dynamic conditional multi-factor premiums from the dynamic conditional multifactor asset pricing model;   wherein the dynamic conditional multifactor model embeds at least six fundamental factors including market risk, size, value, momentum, asset growth, and operating profitability.   
     
     
         3 . The dynamic conditional asset pricing system of  claim 2 , wherein
 the dynamic conditional multifactor model is a dynamic asset pricing model with recursive filtration for dynamic conditional alpha and beta estimation on the primary basis of primary financial data from the database, and wherein the dynamic conditional asset pricing system includes a baseline static alpha and beta generation module for ordinary-least-squares (OLS) estimation of static single-factor and multi-factor premiums.   
     
     
         4 . The dynamic conditional asset pricing system of  claim 2 , further comprising:
 a conditional specification test module configured to distinguish both static and dynamic conditional multifactor asset pricing models for individual risky assets or asset portfolios;   wherein the conditional specification test module generates χ 2  test statistics and p-values for quantitative static or dynamic conditional asset pricing model affirmation.   
     
     
         5 . The dynamic conditional asset pricing system of  claim 2 , further comprising:
 an internal core statistical processing module, wherein the dynamic conditional asset pricing model can be generalized to fit daily individual asset returns as well as monthly international asset portfolio returns for stocks, bonds, currencies, and commodities.   
     
     
         6 . The dynamic conditional asset pricing system of  claim 2 , wherein the dynamic conditional asset pricing system possesses a core internal model validation functionality for generating dynamic conditional specification test evidence, mean excess return prediction for Top 100 to Top 500 individual risky assets or asset portfolios, average decile excess return assessment for static and dynamic conditional alpha rank, relative model accuracy in contrast to baseline concordance in binary top-quantile risky-asset return prediction, and explanatory power of dynamic conditional alphas in the cross-section of long-run mean excess returns and Sharpe ratios for both individual risky assets or asset portfolios. 
     
     
         7 . An algorithmic financial intelligence technology (fintech) platform, which can be hosted in at least one internal cloud server with cloud computing connections to at least one external cloud server, comprising:
 a dynamic conditional asset pricing system with the core functionalities of both static and dynamic conditional estimation of alpha and beta factor premiums, recursive multivariate filtration, and conditional specification test;   a fintech output module for exporting financial intelligence and visualization including core ratio summary, major financial statement analysis, quadripartite visualization of asset price and return data both over time and in the cross-section, dynamic conditional alpha rank order, key taxonomy of mean excess returns and return volatilities; and   an internal social network interface that connects both the dynamic conditional asset pricing system and the fintech output module within at least one internal server to multiple external social networks;   wherein multiple users interact with one another by transmitting valuable units of financial intelligence, quantitative and qualitative information, text, or other visual content including status updates, posts, comments, favorites, likes, unlikes, dislikes, shares, tags, tracks, saves, invites, notes, messages, traffic statistics, and so forth.   
     
     
         8 . The algorithmic fintech platform of  claim 7 , wherein the external social networks include at least Facebook, Twitter, LinkedIn, YouTube, Google+, Pinterest, Instagram, Reddit, Tumblr, Flipboard, and personal email. 
     
     
         9 . The algorithmic fintech platform of  claim 7 , further comprising:
 an equivalent cloud computing functionality of algorithmic financial intelligence technology platform automation for inducing multiple users to interact with one another on the key basis of financial intelligence and information;   an equivalent financial information set including static and dynamic conditional multifactor premium estimation, multivariate time-series recursive filtration, dynamic conditional alpha rank order, quadripartite visualization of asset pricing and return data both over time and in the cross-section, major financial ratio summary, financial statement analysis, and executive taxonomy of Sharpe ratios, mean excess returns, and standard deviations;   an equivalent virtual intermediary or clearinghouse functionality enabling each user to make use of some initial virtual portfolio dollars or points upon registration in order to sequentially trade risky assets on an at least daily basis; and   an equivalent interactive and modular social network functionality, wherein the algorithmic fintech platform helps optimize active-click mutual engagement (ACME) among active end users through both the centrifugal and centripetal user interactions and the time-dependent rank order of each active user's asset portfolio value ceteris paribus;   wherein in the social network, the ACME rises exponentially when the platform boosts user interactions, raises individual users' dynamic conditional alpha ranks, and causes significant changes in user-specific structural characteristics including demographic attributes, interests, behaviors, and other platform usage patterns.   
     
     
         10 . The algorithmic fintech platform of  claim 7 , wherein the internal cloud server that connects to at least one external cloud server for the core retrieval and acquisition of rich and relevant financial data for each risky asset or risky asset portfolio from multiple external data sources including Yahoo Finance, Google Finance, Reuters, FINVIZ, and so on. 
     
     
         11 . The algorithmic fintech platform of  claim 7 , wherein the fintech platform executes a primary maintenance method, and wherein the primary elements of this maintenance method include curation, marquee, piggyback, and seed, and wherein:
 the curation element refers to the primary need for the platform orchestrator to filter out any undesirable social network results, patterns, and even conflicts including abusive usage and inappropriate content circulation;   the marquee element refers to backend risky asset trade history that the Top 100 alpha users sequentially adapts to gain the highest up-to-date virtual portfolio dollars or points, wherein many other users who surf and trade on the fintech platform can gradually learn from the backend history of profitable risky asset trades;   the piggyback element refers to the essential need for the platform orchestrator to allow end users who currently surf on some external social-network platforms to gravitate toward the fintech platform, wherein these other platforms can be external social networks including at least Facebook, YouTube, Twitter, LinkedIn, Pinterest, Google+, Instagram, Reddit, Tumblr, Flipboard, and personal email; and   the seed element refers to the modular and interactive process in which the Top 100 alpha users engage in the progressive production and diffusion of valuable units of both financial intelligence and information through their platform interactions and activities.   
     
     
         12 . The algorithmic fintech platform of  claim 7 , wherein the graphical user interface connects financial intelligence including dynamic conditional alpha rank to both Top 500 individual risky assets, Top 500 international risky asset portfolios, and Top 100 alpha users to create tangible standalone value for all the active users who have yet to engage and interact on the fintech platform. 
     
     
         13 . The algorithmic fintech platform of  claim 7 , wherein the algorithmic fintech platform allows each end user to switch among at least three different structural methods of global news feed customization, wherein at least three different structural method of news feed customization includes three news feed customization modules comprising:
 a user-experience module for all active users to learn from the sequentially profitable asset trade histories of Top 100 alpha investors who make the most productive use of their virtual portfolio dollars or points;   an investment-style module providing numerous channels that segment individual users into risky asset portfolio tilts including size, value, momentum, and so on; and   a social community module embedding multiple groups that represent unique clusters on the basis of demographic attributes, interests, behaviors, and other platform usage patterns.   
     
     
         14 . The algorithmic fintech platform of  claim 7 , wherein the fintech platform tracks and records each end user's risky asset trade activities and then updates each user's virtual portfolio dollars or points at high frequency to rank all of these users who surf and trade on the fintech platform. 
     
     
         15 . The algorithmic fintech platform of  claim 7 , wherein the fintech platform regularly updates the Top 100 alpha investor checklist with sequentially profitable asset trade histories for all active users to learn from the modular and interactive social network mechanism. 
     
     
         16 . The algorithmic fintech platform of  claim 7 , wherein the fintech platform sorts and provides a useful and scalable checklist of core output files including dynamic conditional alpha output and rank order, conditional specification test evidence, incremental enhancement in model accuracy relative to baseline concordance, average excess return and return volatility for each individual risky asset or asset portfolio, Sharpe ratio for conditional reward-risk analysis, quadripartite visualization of asset return frequencies and distributions, asset price time-series output, asset return time-series output, core financial ratio output, and financial statement analysis.