Search

Kriptovalute so najbolj aktualen način vlaganja denarja v današnji digitalni dobi, predvsem zaradi njihove dostopnosti. A kot pri vseh sredstvih je vlaganje v kriptovalute bolj zapleteno, kot si najprej predstavljamo. Če hočemo biti pri trgovanju uspešni, moramo uporabiti preverjene metode trgovanja. Trgovanje lahko razdelimo na dva dela: strateški in taktični del. Za strateški del naloge bomo implementirali metodo optimizacije portfelja na podlagi razmerja Sharpe in razmerja Sortino. Za taktični del naloge bomo uporabili metodo ARIMA+GARCH za predvidevanje cen in napoved nakupov. Trgovalno strategijo bomo testirali s preverjanjem nazaj. Strategija bo uspešna, če bo bolj dobičkonosna in manj volatilna kot metoda ''kupi in drži'' z bitcoinom. Ugotovili smo, da tako strateški kot taktični del večata dobičke. Dobički v naši testni periodi so približno 2,27-krat večji kot pri metodi ''kupi in drži'' z bitcoinom. Taktični del strategije pa zagotavlja manjšo volatilnost glede na bitcoin. Volatilnost je približno 8 % manjša v primerjavi z metodo ''kupi in drži'' z bitcoinom, čeprav je strateški del strategije povečal volatilnost za 14 % glede na bitcoin. Cryptocurrencies are the most current means of investing money in the current digital era, especially because of their accessibility. But as with any funds, investing is harder than we first imagine. If we want to make a successful investment, we have to use proven methods of investment. We roughly divide investing into two parts: Strategy and Tactics. For the strategical part, we will implement a portfolio optimisation method, based on the Sharpe ratio and Sortino ratio. For the tactical part, we will use the ARIMA+GARCH method for price and trading forecasting. We will test the method via a Backtest. The strategy is successful, if it is more profitable and less volatile than a buy & hold method on Bitcoin. We uncovered, that both Strategy and Tactics make our strategy more profitable. The profits are about 2,27 times that of the buy & hold method on Bitcoin in our testing period. The tactical part of the strategy provides less volatility relative to Bitcoin. Volatility is about 8 % lower relative to the buy & hold method on Bitcoin, despite the strategical part of our trading strategy increasing volatility by 14 % relative to Bitcoin.

In this work we are collecting and processing basic information that allows us a general overview of the five most popular global, free market economies which are: the exchange of currency (foreign exchange), rare metals, stocks, residential real estate and the cryptocurrency markets. By having more in-depth knowledge about the possibilities that each separate financial instrument or asset offers we are devising a strategy suited for different types of investors. Any economical system in its most natural state will create some sort of dynamic it is possible to draw parallels between the free-economy and the Lotka-Volterra predator behaviour equations, which can help us to better visualise the behaviour of such systems. We try to compare the model with real life scenarios that had greatly affected some of the economies, one of them being the popularization of cryptocurrencies in 2017 and the other being the most recent Coronavirus epidemic. By having a rough comparison we can see a clearer picture of the practical uses and the necessities of having an increased and better selection of such models. V tem delu bomo zbrali podatke ter si ogledali postopke, ki nam dovoljujejo splošno spoznanje petih svetovno najbolj razširjenih prostih trgov: valutni trgi, drage kovine, delniški trgi, nepremičninski trgi ter kriptovalutni trgi. S poglobljenim znanjem o možnostih, ki jih vsak finančni instrument povezan s posameznimi trgi ponuja, lahko sestavimo optimalno investicijsko strategijo za tri različne vrste investitorjev. Prav tako vsi prosti ekonomski sistemi že sami po sebi ustvarijo svojevrstno dinamiko. Ogledali smo si vzporednice, ki jih je mogoče potegniti med prostimi trgi ter Lotka-Volterra enačbami za opisovanje dinamike med plenom in plenilcem. To nam omogoča boljši vpogled v vedenje sistema ter ponuja možnosti za grobo napovedovanje sprememb sistemu.

Namen naloge je izpeljati in pojasniti model povečanja kapitalnih dobrin. Ta model sem v nadaljevanju še nekoliko nadgradil in s tem postavil v okolje, v katerem se tehnologija prenaša med državami. Na koncu pa sem glavne ugotovitve pri osnovnem modelu še simuliral v Matlabu in vanje vstavil še realne podatke, pridobljene s spleta. Skozi vse dele sem želel preveriti ali med državami prihaja do dohitevanj, ter dokazati ravnotežno rast in pokazati na podatkih, da se države ravnotežnemu stanju približujejo. Skozi teoretični del izpeljave osnovnega modela je bilo mogoče opaziti, da v njem nastopa veliko spremenljivk, ki vplivajo na gospodarsko rast. Glavne spremenljivke, ki sem se jim posvetil v simulaciji, so obseg zaposlitve, vlaganje v raziskave in razvoj ter število različic tehnologije na trgu. Poleg tega pa sem preveril še vpliv spremembe razmerja med kapitalom in delom na rast. Na koncu izpeljave sem dobil dva glavna rezultata: stopnjo gospodarske rasti ter stopnjo rasti potrošnje. Ob tem pa sem dobil še nekaj omejitev, ki morajo veljati da model drži. V simulaciji sem uspel ugotoviti, da tako v resnici, kot tudi skozi simulacijo modela prihaja do dohitevanja med razvitimi državami in državami v dohitevanju. Prav tako pa mi je uspelo pokazati, da se države približujejo nekemu ravnotežnemu stanju oziroma ravnotežni rasti. In my paper, I intended to show and explain the model with an expanding variety of products. Then I further expanded it to set it into an environment, where there is a flow of good between countries. In the end, I took the main findings from the base model and simulated them in Matlab. I then found real-life data online and inserted it in the findings. Through this whole process, I tried to show that there is catch-up between countries as well as to prove the existence of steady-state growth and that countries are actually converging towards it. In the first, theoretical, part of my paper, it was evident that there are many variables, that have an influence on economic growth. The main variables that I paid special attention to in my simulation, are labour input, investment in research and development and the number of varieties of goods on the market. On top of that, I looked into the effects of the labour to capital ratio on growth. At the end of the theoretical part, I got two main results: growth rate of the economy and the gowth rate of consumption. During the this part, many restrictions appeared, that have to be taken into account, if the model is to be correct. In the simulation part of my paper, I was successful in proving that, in real life, as well as in the model, there is catch-up between developed and developing countries. I also managed to show that both types of countries are converging to a steady state and a steady state growth.

Peptidi so zaporedja aminokislin. če imajo strukturni motiv obvite vijačnice, lahko predvidimo njihovo povezovanje in ustvarimo večje strukture. Za sestavljanje stabilne periodične mreže uporabljamo verige peptidov, ki jih v matematičnem modelu predstavimo kot graf verižne strukture. Tega vlagamo v ravninsko mrežo, da zagotovimo željeno obliko. Relacije med členi odločajo o tem, ali se bodo ti sami sestavili v mrežo. Matrika relacij je zaradi uporabe ortogonalne množice členov permutacijska. če upoštevamo pogoje iz narave, stabilnosti in oblike mreže, lahko možnosti še dodatno omejimo. Predlagamo omejitev območja iskanja periodične mreže ter preverjanja enoličnosti. Predstavimo algoritem z lenim vlaganjem, kjer mrežo gradimo s postopnim povezovanjem členov, ki jih vlagamo, ko jim za to preostane le ena možnost. Peptides are sequences of amino acids. In case of their coiled coil structural motif we can predict what bonds will form and create larger structures. We use chains made of peptides for assembly of stable periodic nets. We represent peptides as a chain structure graph in our mathematical model, which we embed into a planar net to guarantee the proper form. Relations between links given in a relation matrix decide whether or not links will self-assemble into a net. Because of the use of orthogonal link sets, relation matrices are permutation matrices. We can further limit relation matrices by applying several conditions derived from nature, stability and net shapes. For the search of periodic nets and uniqueness check we propose area restrictions. The presented lazy embedding algorithm connects one pair of links in a step and embeds them only if there is only one embedding option left.

V diplomskem delu smo želeli ugotoviti, kakšno je stanje z investicijami v logistiki in njihovim upravljanjem. Izbrano temo smo si izbrali kot naš predmet raziskovanja, saj gre za dokaj neraziskano področje, o katerem še ni veliko napisanega in ugotovljenega. Raziskavo smo izvedli na ozemlju celotne Slovenije. Anketirali smo 20 podjetij, ki se razlikujejo med seboj po sektorjih, velikosti in lasti. Anketa, ki je glavno vodilo naše raziskave, je sestavljena po vzoru poročila Global Status Report on the Governance of Enterprise IT iz leta 2011 inštituta ITGI. Gre za poročilo, ki ugotavlja, kakšno je upravljanje investicij na področju informacijskih tehnologij. Glede na to, da sta si IT in logistika v veliko lastnostih podobni, je anketa prirejena na način, da lahko ugotovimo, kakšno je upravljanje investicij v logistiki. Podjetja smo povprašali po vlogi vodje logistike, o njihovih poslovnih ciljih, kaj menijo o vlogi logistike, ali vlagajo v investicije v logistiki, ali pri tem uporabljajo kakšne okvirje oz. standarde ipd. Prva in edina takšna raziskava je bila narejena leta 2012. Odločili smo se jo ponoviti, z namenom, da ugotovimo, kako se je stanje spremenilo. Ugotovili smo, da podjetja sicer menijo, da je logistika v podjetju zelo pomembna, vendar je še vedno ne vidijo kot ključni faktor pri doseganju poslovnih ciljev. Prav tako smo ugotovili, da je potrebno vpeljati okvir, ki bi vodjem pomagal pri upravljanju investicij v logistiki, saj je na tem področju še veliko neznanja. In this thesis we wanted to investigate the situation of investments in logistics. The reason for choosing this topic is that it is yet not very well researched and there are not many publications available. The research was done across Slovenia. Twenty companies different in size, ownership and field of expertise were given a survey. Survey was used as the main tool in our investigation and was set up according to Global Status Report on the Governance of Enterprise IT (2011), an investment management report in the field of IT published by ITGI Institute. The companies were asked about what the role of the logistics manager, business goals, role of logistics, the amount of investments they make into it and whether they follow any standards or frameworks. The only research of this type was done in the year 2012. Our aim was to verify it and investigate the changes. Our conclusion is that companies do think that logistics is very important however, they do not see it as the key factor to success. On top of that we have concluded that it is necessary to implement a framework or guideline which would help the leaders to manage investments in logistics. The knowledge and experience in this field are yet scarce.

Še ne tako dolgo nazaj je bilo trgovanje na finančnih trgih rezervirano samo za velike institucije kot so banke, ter različni skladi, ki so upravljali z velikim kapitalom. Z razširitvijo osebnih računalnikov ter zakonsko ureditvijo tega področja so borzne hiše s pomočjo trgovalnih platform omogočile trgovanje tudi manjšim organizacijam ter posameznikom z manjšim osnovnim kapitalom. Potrebno je ločiti med trgovalci in vlagatelji. Trgovalci aktivno upravljajo s svojim premoženjem. Medtem, ko lahko vlagatelji svoj kapital prepustijo v upravljanje drugim. Prav tako so v primerjavi z vlagatelji usmerjeni precej bolj kratkoročno. V pozicije vstopajo in izstopajo zelo hitro. Ta interval lahko znaša od nekaj minut pa do nekaj dni ali tednov. Trgovalci lahko danes tako kar iz domačega naslonjača trgujejo z delnicami, terminskimi pogodbami, izvedenimi finančnimi instrumenti ali valutnimi pari. Trgovanje je na videz precej preprosto, pogosto pa ljudje v tem vidijo sredstvo hitrega zaslužka. Nepisano pravilo pa pravi, da kar 95 odstotkov trgovalcev svoj vložek izgubi v nekaj tednih ali mesecih. Za uspešno trgovanje je pomembno razumevanje treh konceptov. To so torej tehnična in temeljna analiza ter psihologija trgovanja. V tem diplomskem seminarju bomo podrobneje obdelali pomembnost poznavanja psihologije za uspešno trgovanje ter ponudili nekaj rešitev kot pomoč za uspešno in dobičkonosno trgovanje. Not so long ago trading was reserved for big financial institutions like banks and hedge funds with large capital. Along with occurrence and expansion of personal computers brokerage houses started to offering trading platforms targeting smaller investors with smaller amount of money. It is important to distinguish between traders and investors. Traders are actively managing their portfolio and are trading on a much smaller time frame than investors. That time frame could be anywhere from few minutes to few days or weeks. Investors on the other hand can leave other institutions or professionals to handle their funds for them. Also are they usually more long term oriented than traders. Trading nowadays is so accessible that anyone can trade from their home desk. Traders are usually trading with equities, futures, options, currencies. On first sight trading can look very easy and simple. This illusion can be a hidden trap for many people on their early journey into the trading world. Very well known unwritten rule is saying that around 95 percent of all traders lose their money in first weeks or months of trading. Therefore, for a successful trading we need to know three key concepts. These concepts are: fundamental analysis, technical analysis and trading psychology. In this work we are writing about importance of knowing key concepts of trading psychology for successful trading. Alo we will offer some solutions to the most common problems occurring during trading.