Luboš je vedoucí pobočky Písek a zároveň firemní právník s bohatými zkušenostmi v oblasti ochrany osobních údajů, kapitálových trhů a e-commerce v České republice. Luboš vystudoval Práva na Masarykově univerzitě v Brně a je rovněž specialistou na oblast veřejných zakázek.
Jiří je jakožto zakladatel a předseda představenstva společnosti zodpovědný za rozvoj a směřování firmy. Absolvoval Vysokou školu ekonomickou v Praze, fakultu podnikohospodářskou. Působí v Radě pro komercionalizaci při biologickém institutu Akademie věd, je rovněž předsedou Asociace pro komunikační nástroje a internet věcí, z.s., která sdružuje organizace působící v oblasti IoT a AI.
Public and private customers increase online spend every year. As new generations of buyers mature there are more and more demands for goods and services available online for suppliers.
In Czech market there are approximately 20 portals that mediate demands with proper suppliers. Customers are promised to find qualified, relevant suppliers that have proper experience and references in exchange for contact details and description of demand. Which is more advanced service than look for proper supplier using web search engines like google.com or seznam.cz but less complex service than using online auction portals that remain domain of enterprise companies purchase departments.
Business mediation portals
Services of mediation portals are free for buyers, yet suppliers are mostly asked for various payments.
When it comes to public customers that are obliged to conduct public tenders in order to find proper supplier – monthly or yearly fees are asked to get up-to-date information about new tenders that appear on thousands of public subject profiles. Such service sends notifications about description and subject of tender to suppliers that match their profile filled during registration and start of service.
Weaknesses of such service are obvious – in case that description and subject of public tender does not match the profile – supplier gets no notification. Which is followed by missing important opportunities in cases that proper description is provided in following attachments and documentation mostly at more complex deals.
In case of private demands suppliers are asked to pay for each demand they are interested in. Such procedure in practice means that suppliers are overwhelmed by small inquires that require payments for contact disclosure.
Mediation by Artificial Intelligence
State of the market remains suboptimal as customers do not get information on the most relevant suppliers (as their demand is shown only to paying ones) and suppliers miss some of the opportunities as well as well as they need to handpick relevant demands for their services.
Artificial Intelligence that would be able to understand:
- texts of offers of suppliers out of their websites, product information and descriptions of services provided by marketing materials or presentations and
- texts of inquiries provided by procurement documentation or simply by text generated by customers
has potential to bring significant innovation in the field. Value added may bring:
- weighted match of inquiry profile and supplier profiles
- more relevant customer demand and supplier references match
- checks of qualification criteria for public tenders
- reduction of administration during registration processes
- supplier matches that are independent on payments from supplier side
- valuable market research information
Artificial Intelligence offering
No matter potential benefits our marketing research showed that mediation portal operators have no interest in such innovation in their field.
Our research showed that mediation portal operators are mostly unknown or unavailable – which is understandable as their services do not satisfy many of their clients even though most of businesses have been offered such services (even by more of mediation portals).
Out of mediation portal operators reached none of them develops business by way of product development – preferred way of development is customer acquisition.
In the introductory article of the Reviews section, we present the Multicriterial Text Analysis Software (MTA) project, which deals with the removal of information asymmetries in news and reviews.
The MTA team of scientists from CYRRUS ADVISORY, a.s. and Mendel University in Brno uses machine learning methods to analyze text in the field of current news and product reviews.
In the following posts, you can look forward to describing the research issues and the results we have already achieved in this area.
This post aims to give information our team is currently dealing with – a investment approach used by hedge funds and proffesional portfolio managers. There are such investment approaches that do not require crystal balls to expose investment opportunities, but which are base on statistics, economic operators, logical relationships and the professional use of state-of-the-art technologies. A key fact is that there is no use of the prediction of future values for estimating the future direction of the asset analysed, but the approach seeks anomaly (market opportunities) in the relationships of different asset prices among which there is a certain (economic, political, commercial, technical) logical relationship. The result is a investment strategy which does not estimate the direction of the future development of the asset but which is able to identify the business opportunity created in real time.
Our goal is to create/find alpha factor that gives an investor an advantage over a normal approach where an investor buys and holds a security until its maturity or sale. One source of this alpha factor is „Relative Value Trading“, which contains many investment approaches, including Statistical Arbitrage, Convertible Arbitrage, Fixed Income Arbitrage, Equity Market Neutral, Spread Trading, Pair Trading. The point of this approach is to track the logical links among the selected assets (we used 30 randomly selected U.S. shares) and the trading of anomalies that appear over time on those relationships.
A wide range of underlying assets (shares, commodity futures, bonds, government bonds and CDS, convertible bonds and their underlying shares) can be used to analyse the Relative Value.
For our initial analysis, we used 30 randomly selected U.S. shares, which are currently mainly used to compile and verify the correctness of the methodology and operating process.
Once the operating process has been detuned, we will also deploy the application to other data sources.
The Relative Value Approach is a mean-reverting investment approach where the underlying premise is a stable relationship between two or more assets.
This relationship is identified by a long-term correlation matrix of differentiations of logarithmic price of the assets entering the analysis.
These short-term anomalies provide an alpha factor on which to build a investment strategy used by proffesional portfolio managers.
Table 1: Correlation matrix of significant relationships
Figure 1: Correlation coeficients heatmap
The chart above illustrate the results of the first step. Of the 30 titles analysed, we identified 2 bilateral long-term relationships formed by JPMorgan Chase & Co. (JPM) with Goldman Sachs Group Inc (GS) and Exxon Mobil Corporation (XOM) with Chevron Corporation (CVX).
In the second step, we culled a short-term sliding correlation with a period of 10 business day, for these two relationships. The goal was to trace short-term deviations from long-term normal which will be further used as a alpha signals for a investment strategy. The results are shown in the charts bellow.
Figure 2: Comovements and short-term disruption in relationship „XOM-CVX“
Figure 3: Comovements and short-term disruption in relationship „JPM-GS“
On both lower charts you can see short-term decreases in correlation coeficients, which we consider to be signs of short-term deviations from long-term normal. We regard these short-term deviations as a investment opportunity, the automated use of which will our team further address.
This post aims to outline the idea of one of the investment approaches by hedge funds and professional portfolio managers and to show the results achieved by us. Further steps (robustness and stability checks of the long-term correlation matrix) will be analysed in other positions, which we are curently dealing with in the search for optimisation solutions in assets management in detail, including the technological processes.
Our goal is to create a portfolio of financial assets that yields a positive return even at market corrections.We develop a platform that uses Bayesian statistics and AI knowledge (primarily neural networks) to effectively rebalance the portfolio of business strategies (taking into account all aspects of risk management, such as portfolio engagement based on mutually correlated titles, currency, business strategy – all in order to rebalance the portfolio according to the given requirements).
The platform will be able to identify situations where there is low / high liquidity on the market and, in the light of this, to disable / deploy some trading strategies, modify money management, or avoid trading completely.
Output is a bot – an actively managed portfolio that exhibits lower risk (measured by maximum drawdown) with higher yield stability, without active trader / developer intervention. The potential of the platform is to work in multiple versions of the user’s IT capability – from a very variable user environment, where the programmer can create and manage a tailor-made portfolio to a black box (lite version) where the user chooses from several preferred criteria and the bot will, according to selected criteria, compose the portfolio itself.