Design and testing of stand-specific bucking instructions for use on modern cut-to-length harvesters

This study addresses three important issues in tree bucking optimization in the context of cut-to-length harvesting. (1) Would the fit between the log demand and log output distributions be better if the price and/or demand matrices controlling the bucking decisions on modern cut-to-length harvesters were adjusted to the unique conditions of each individual stand? (2) In what ways can we generate stand and product specific price and demand matrices? (3) What alternatives do we have to measure the fit between the log demand and log output distributions, and what would be an ideal goodness-of-fit measure? Three iterative search systems were developed for seeking stand-specific price and demand matrix sets: (1) A fuzzy logic control system for calibrating the price matrix of one log product for one stand at a time (the stand-level one-product approach); (2) a genetic algorithm system for adjusting the price matrices of one log product in parallel for several stands (the forest-level one-product approach); and (3) a genetic algorithm system for dividing the overall demand matrix of each of the several log products into stand-specific sub-demands simultaneously for several stands and products (the forest-level multi-product approach). The stem material used for testing the performance of the stand-specific price and demand matrices against that of the reference matrices was comprised of 9 155 Norway spruce (Picea abies (L.) Karst.) sawlog stems gathered by harvesters from 15 mature spruce-dominated stands in southern Finland. The reference price and demand matrices were either direct copies or slightly modified versions of those used by two Finnish sawmilling companies. Two types of stand-specific bucking matrices were compiled for each log product. One was from the harvester-collected stem profiles and the other was from the pre-harvest inventory data. Four goodness-of-fit measures were analyzed for their appropriateness in determining the similarity between the log demand and log output distributions: (1) the apportionment degree (index), (2) the chi-square statistic, (3) Laspeyres quantity index, and (4) the price-weighted apportionment degree. The study confirmed that any improvement in the fit between the log demand and log output distributions can only be realized at the expense of log volumes produced. Stand-level pre-control of price matrices was found to be advantageous, provided the control is done with perfect stem data. Forest-level pre-control of price matrices resulted in no improvement in the cumulative apportionment degree. Cutting stands under the control of stand-specific demand matrices yielded a better total fit between the demand and output matrices at the forest level than was obtained by cutting each stand with non-stand-specific reference matrices. The theoretical and experimental analyses suggest that none of the three alternative goodness-of-fit measures clearly outperforms the traditional apportionment degree measure. Keywords: harvesting, tree bucking optimization, simulation, fuzzy control, genetic algorithms, goodness-of-fit

Aktuaariraportti 2011: Kelan hoitama sosiaaliturva 2010–2060

Sivumäärä 69

Suomalaisten julkisesti listattujen senttiosakkeiden suoriutuminen verrattuna OMX Helsinki Small Cap -indeksin muihin osakkeisiin

Senttiosakkeista tehtyjä tutkimuksia on olemassa hyvin rajoitetusti, ja ne ovat keskittyneet lähinnä senttiosakelistautumisiin. Tässä tutkielmassa tarkastellaan suomalaisia julkisesti noteerattuja senttiosakkeita ja niiden suoriutumista kymmenen vuoden ajanjaksolla vuosina 2006–2015. Tavoitteena oli selvittää, onko suomalaisiin julkisesti noteerattuihin senttiosakkeisiin sijoittaminen kannattavaa toimintaa ja minkälaisia tuottoja on odotettavissa senttiosakkeisiin sijoittamalla. Tutkimusaineisto koostui tutkielmassa tehdyn määritelmän mukaisista senttiosakkeista ja muista Small Cap -indeksin osakkeista, joita kutsuttiin puolestaan ei-senttiosakkeiksi. Tuotot laskettiin osakkeiden päivittäisistä tuottoindekseistä. Tuottoja verrattiin lyhyellä, keskipitkällä ja pitkällä aikavälillä. Tuottojen tarkastelun tueksi senttiosakkeille ja ei-senttiosakkeille laskettiin seuraavat menestysmittarit: Sharpen luku, Treynorin indeksi ja Jensenin alfa. Lopuksi verrattiin vielä seuraavia tunnuslukuja: ROE (%), E/P-luku, P/B-luku, osinkotuotto-% ja velan suhde omaan pääomaan (%). Saatujen tulosten perusteella suomalaiset julkisesti noteeratut senttiosakkeet ovat lyhyellä aikavälillä kannattavia sijoituskohteita, mutta mitä pidemmäksi tarkasteluperiodi kasvoi, sitä huonommin ne suoriutuivat. Lisäksi senttiosakkeet hävisivät kaikilla tarkasteluperiodeilla ei-senttiosakkeille. Suurimmat positiiviset tuotot olivat kuitenkin yksittäisillä senttiosakkeilla. Senttiosakkeisiin havaittiin liittyvän paljon riskejä, kuten suuri volatiliteetti, suuret negatiiviset tuotot ja konkurssin mahdollisuus. Myös kaikki menestysmittarit ja tunnusluvut indikoivat senttiosakkeiden olevan ei-senttiosakkeita huonompia sijoituskohteita. Sijoittajien on oltava erityisen tarkkoja senttiosakkeiden kanssa, sillä niihin sijoittaminen on pitkälti verrattavissa uhkapelaamiseen.

Low-Volatility Anomaly in Helsinki Stock Exchange

International research shows that low-volatility stocks have beaten high-volatility stocks in terms of returns for decades on multiple markets. This abbreviation from traditional risk-return framework is known as low-volatility anomaly. This study focuses on explaining the anomaly and finding how strongly it appears in NASDAQ OMX Helsinki stock exchange. Data consists of all listed companies starting from 2001 and ending close to 2015. Methodology follows closely Baker and Haugen (2012) by sorting companies into deciles according to 3-month volatility and then calculating monthly returns for these different volatility groups. Annualized return for the lowest volatility decile is 8.85 %, while highest volatility decile destroys wealth at rate of -19.96 % per annum. Results are parallel also in quintiles that represent larger amount of companies and thus dilute outliers. Observation period captures financial crisis of 2007-2008 and European debt crisis, which embodies as low main index annual return of 1 %, but at the same time proves the success of low-volatility strategy. Low-volatility anomaly is driven by multiple reasons such as leverage constrained trading and managerial incentives which both prompt to invest in risky assets, but behavioral matters also have major weight in maintaining the anomaly.

Cascade hydropower capacity increase profitability analysis with optimisation modelling

The electricity market and climate are both undergoing a change. The changes impact hydropower and provoke an interest for hydropower capacity increases. In this thesis a new methodology was developed utilising short-term hydropower optimisation and planning software for better capacity increase profitability analysis accuracy. In the methodology income increases are calculated in month long periods while varying average discharge and electricity price volatility. The monthly incomes are used for constructing year scenarios, and from different types of year scenarios a long-term profitability analysis can be made. Average price development is included utilising a multiplier. The method was applied on Oulujoki hydropower plants. It was found that the capacity additions that were analysed for Oulujoki were not profitable. However, the methodology was found versatile and useful. The result showed that short periods of peaking prices play major role in the profitability of capacity increases. Adding more discharge capacity to hydropower plants that initially bypassed water more often showed the best improvements both in income and power generation profile flexibility.