Robin Gras’ Post

March 18th 2024 - I am working on my financial analysis on 14th floor. So here is my sushi 🍣 economics so far - those interested in this can ask me for diagrams and extensive notes. https://lnkd.in/dges3e98 See my take on Russian stock market in simple way. See MOEX for example as benchmark. A lot of firms trade at discount to book value. RSX does options trading too and is related it seems my research is showing to MOEX. Assets are also highly tied to commodity prices. Russia 🇷🇺 is also an exporter of oil. Golden eggs you know some economies have to export some commodities a lot- such as some regions are wheat basket because they export a lot of wheat. Some areas export soybeans a lot and their prices are shown-#El Nino can impact supply chains. Here is my takeaway from random walk down wall street-it has a funds framework and a vast survey is included to scrutinize how funds perform. Often funds are seen to be beating indexes. I call it tip of iceberg. Of all funds-some funds are like that.There is an example suppose 75 percent of fund is in equities and 25 percent bonds-then an investor can move some from stocks and put in bonds -usually portfolios have a mix of both assets.This is simple way of rebalancing. Sometimes bonds are a place to hide means they are good diversifier- On psychology side-thinking fast and slow is relevant. Kahneman is often compared for reframing work he did -with Ivan Pavlov. It shows cognitive bias and all these things that play a role in finance ▶️- He works on intuition genre-how humans do intuitive thinking-and he describes system 1 (innate) and system 2. He mentions Herbert Simon too. One lesson is what-intuition can do for deconstruction of markets. Also-how intuition helps stock pickers in that activity. He recommends we can be reading an expert Joshua Foer. He brings up examples from ancient Greece. One method he explains is of #mental routes and I would praise that a lot. One useful exercise he says is of weekly tiles often -yeah weekly posters. According to Ray Dalio without pursuing dream life is mundane. I was discussing butterfly 🦋 arbitrage to link it to economic landscape. See context for Chinese yuan. Finally let us explore link between Japanese yen and JGBs that is ascribed. Let me bring a small anecdote-Joel Greenblatt started in 1985 gotham fund. He got really high returns-he ran fund in one year and for nine more years hence in nutshell for total ten years. He uses a magic sauce based on intelligent investor and all. Such as cheap stocks with high earning yield. Everyone thinks he is stock market genius and I fully agree. I can try m-stock.

Just finish reading a high-quality article with an 'old' topic---herding behaviour in financial market, but with new logics and data. It attributes the divergence of dual structural financial market and economic market to herding behaviour: optimism and pessimism with two main logics with NAAISM index and vs Gross Value added data. My opinions: Financial market or capital market is perfect place for demonstration of herding behaviour and experimental place for it. 1. The investment and evaluation is based on the expectation, this is inherent nature of all financial models, evaluations and macro capital market forecasts. Most of the rational investments are still based on this as foundation and then reacts to the information inputs. So even rational expectation have to be optimistic or pessimistic, naturally. 2. A large proportion of the investment is not based on point 1, but no completely because they lack of the capability of following the modelling path especially for institutional investors---A significant capital market participants have their own goals, self-positions and natural limits to drive their capital movement. Not completely herding behaviour. 3. Government intervene did work as a guideline or at least indicators. The article's instances that the market ignored the government intervene are only in the extreme capital market conditions. Normally financial model and reaction to event driving are functioning. 4. I think philosophical relativity and self-realization effects and circle are very strong in a lot of financial cycles and financial phenomena. This is not just realized through financial behaviours but through rational cycle with all financial market participants---they are all organic components and some of them are very rational components in this system. I may deepen it further in the future.

#Macro_Modeling In my research, I've always found the history of macro modeling fascinating. So, where did it all begin? In November 1982, FINN E. KYDLAND and EDWARD C. PRESCOT authored an article titled "TIME TO BUILD AND AGGREGATE FLUCTUATIONS," published in Econometrica. This article marked the inception of Real Business Cycle (RBC) models, also known as Non-Keynesian models. These models, rooted in neoclassical economics, incorporate shocks, specifically, shocks caused by changes in productivity, essentially technological shocks (Business Cycles are triggered by real shocks). RBC models, being neoclassical, assume perfect competition, flexible prices, and no role for monetary policy (money is considered neutral). This foundational work ignited a vigorous ongoing debate, prompting enhancements in RBC models. This evolution eventually led to the development of more complex models, known as New Keynesian models, adding richness to the intricate landscape of macro modeling that we observe today. The continuity of the debate subsequently introduced TANK (Two agents) and the elegantly sophisticated HANK models. I'll leave you with the article.

Recently read a paper by Andrew Lo that challenges the idea of the efficient market hypothesis. Lo doesn't argue that EMH is outright wrong or useless, just that it might be better served as a theoretical standard by which to compare real-life market conditions that are exposed to irrational risk-reward decisions by investors as well as transaction costs. Super interesting stuff on the theoretical side of finance, and emblematic of that kind of stuff that drew me to finance as a career path. You can read the paper for yourself here: https://lnkd.in/g6FKtjsT Also, Lo repeatedly cites his own previous work to make his point, which is such a chad move.

Political economists, both mainstream and Marxist, find it difficult to reconcile the «real» and «financial» appearances of capital. The conventional view is that «real» capital is an objective productive entity; that «finance» merely reflects this reality; and that, unfortunately, the reflection is often inaccurate, causing the two to «mismatch». This convention, we argue, is baseless if not fraudulent. First, although economists know full well that «real» capital, comprising different capital goods, cannot have a unique objective quantity – they measure this pseudo quantity anyway, arbitrarily. Second, when they realize that their arbitrary measure of «real» capital differs greatly from the corresponding magnitude of finance, they blame the deviation on invisible fluctuations in intangible capital, investor irrationality and market imperfections. And third, they insist that «real» accumulation drives «financial» accumulation, even though their own measures show that the two processes move in opposite directions!

What are we reading today at Kantox? 📚 A tribute to Daniel Kahneman, who passed away on March 27 (*). The Nobel Prize in Economics recipient (2002) and author of 'Thinking Fast and Slow' (2012) pioneered, together with Amos Tversky, the study of behavioural biases—the systematic, but unconscious errors that result from the mental shortcuts we use when making decisions. A while ago we asked ourselves, very much in the spirit of Kahneman and Tversky, the following question: Can the use of automated FX solutions help currency managers mitigate the impact of the most damaging behavioural biases? We then drew up a list, by no means exhaustive, of some of the most common behavioural biases in currency management: - Conservatism bias. The finance team may be unwilling to recognise the benefits of 'embracing currencies'. This may lead them to restrict investment and constrain the future of the company. - Forward rate bias. The tendency to view the presence of a high interest rate differential between two currencies as an opportunity for a financial gain, rather than as a sign of the underlying risk. - Overconfidence bias. When managing a firm’s currency exposure, being unrealistically sure of themselves might lead managers to speculate in currency markets. - Loss aversion bias. Managers may be taking too much risk in order to avoid what they view as a painful loss, while at the same time being too risk-averse in the face of favourable market opportunities. Conservatism bias may be the most damaging. Despite the fact that FX management, thanks to technology, is not the resource-intensive activity it once was, firms that do not update their beliefs and stick to a handful of currencies may face subpar performance. The forward rate bias, in turn, may create hesitance to hedge currencies such as BRL or MXN, because their higher interest rate translates into a deep forward discount against the likes of EUR or CHF. So here's how #CurrencyManagementAutomation can help: . Powered by business rules aimed at removing errors, automated FX programs allow treasurers to remove currency risk with great precision, making it possible to spur growth by ‘embracing currencies’. . Delaying hedge execution with automated conditional orders is an excellent way to ease concerns about the high cost of hedging in the face of unfavourable forward points. (*) Robert D. Herschey: "Daniel Kahneman, Who Plumbed the Psychology of Economics, Deat at 90", Bloomberg, 27 March 2024.

Are you in the #finance industry and looking to gain a competitive edge? Check out the latest research by Dacheng Xiu and his co-authors on how unconventional datasets can add value to your portfolio. Dive into their findings by clicking on the links below: - https://lnkd.in/gNf2RicX - https://lnkd.in/g6JpCzNs #financeindustry #unconventionaldatasets #portfoliovalue #research #text #finance #machinelearning #economics #econometrics #returns #hedgefund #portfolio

Tips and Strategies to handle a GD A group discussion is based on variety of topics which can be broadly classified into- 1.      Factual 2.      Abstract. Given below is the strategy to tackle these two types of GD topics: 1. Factual: These topics are based on static or dynamic components of the environment (such as social, political, economical, etc.). In other words, these topics check your general knowledge as well as understanding of current happenings. The first step towards approaching a factual topic is to analyse the topic from multiple angles. Try to explore the history of the topic- find the people/events that triggered the issue. Discuss the pros and cons from various perspectives. Give examples to justify your viewpoint. Find the stake holders in the given issue- figure out the elements that are going to lose or benefit from the issue. Generally, the factual topics belong to the following areas- education, sports, social, political, economics, IT, environment, national & international events etc. Examples of factual topics- The Euro crisis: issues and challenges, Genesis of Caste System in India etc. 2. Abstract: These topics assess your perception and interpretation on wide-ranging subjects. The way you look at the topic decides your subsequent participation and performance in a GD. The challenge here is to put forth multiple implications of the topic and then evoke a discussion on some of them. Idea generation and ability to add a tangible angle to an otherwise abstract topic are essential to ace such kind of GDs. Think creatively on the topic and then translate that thinking into concrete ideas and words. A creative idea is best justified if it is supported with facts or linked to a current event. Pay attention to other candidates’ points so that they can be developed further. These topics have a wide scope. So, try to explore them from various aspects. 6 Use POPBEANS approach for idea generationPeople, Objects, Place, Behaviour, Event, Actions, Nature, Society. Examples of abstract topics: Smart work v/s Hard work, Modernization is not westernization, Red, Borderless world – a myth/reality? etc.

Exciting news! I am happy to share that my paper titled "Parameter Learning in Production Economies" (co-authored with Roman Kozhan) has been accepted for publication in the Journal of Monetary Economics! This achievement marks the publication of the second chapter of my dissertation in a top-tier journal! The accepted version of the manuscript can be accessed via the following link: https://lnkd.in/dHE692Jc #JME #ParameterLearning #BusinessCycles #RBC

The Magic of the Heterogeneous Agent Model:  Modeling Market Dynamics and Policy Impacts The Heterogeneous Agent Model (HAM) in economics offers a vibrant lens to simulate and understand the diverse behaviours of market participants and the varied effects of economic policies 📊. Here's a closer look at its foundational assumptions and core mathematical formulations: Key Assumptions of HAM 🎭 Heterogeneity of Agents - HAM is based on the idea that agents (individuals, firms) differ significantly in their economic behaviours and choices, reflecting real-world diversity. 🧑💼 Agent Interactions and Adaptability - The model assumes that agents interact with each other and adapt their strategies based on market dynamics and personal objectives. Core Formulation Method 🧮 Central to HAM are stochastic differential equations, enabling the dynamic modelling of diverse economic behaviours. Applications in Economic Sectors: 🌟 Labor Market Analysis: HAM explores how various worker groups with unique attributes react to economic shifts and policy changes. 📈 Financial Market Dynamics: The model sheds light on different investor behaviours and their collective influence on market trends. The Heterogeneous Agent Model (HAM) represents a significant advancement in economic analysis, standing at the intersection of complexity science and behavioural economics. Meanwhile, HAM offers a more realistic approach to economic modelling, allowing for an in-depth simulation of individual actions and the intricate effects of policies across various sectors. #EconomicModeling #Complexityscience #PolicyAnalysis #MarketDynamics